From 8a523e31020658643b2ec9502f6de3bc1cb00e7c Mon Sep 17 00:00:00 2001 From: akorosov Date: Wed, 27 Sep 2023 14:11:33 +0200 Subject: [PATCH 01/25] add get_swath_id_vectors, get_apg_vectors and update other methods --- s1denoise/sentinel1image.py | 126 ++++++++++++++++++++++++------------ 1 file changed, 83 insertions(+), 43 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index a0ba6e3..a22a7c4 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -95,13 +95,12 @@ def __init__(self, filename, mapperName='sentinel1_l1', logLevel=30): self.IPFversion = float(self.manifestXML.getElementsByTagName('safe:software')[0] .attributes['version'].value) if self.IPFversion < 2.43: - print('\nERROR: IPF version of input image is lower than 2.43! ' - 'Noise correction cannot be achieved using this module. ' - 'Denoising vectors in annotation file are not qualified.\n') - return + print('\nERROR: IPF version of input image is lower than 2.43! \n' + 'Denoising vectors in annotation files are not qualified.\n' + 'Only G_TOT-based denoising can be performed\n') elif 2.43 <= self.IPFversion < 2.53: - print('\nWARNING: IPF version of input image is lower than 2.53! ' - 'Noise correction result might be wrong.\n') + print('\nWARNING: IPF version of input image is lower than 2.53! \n' + 'ESA default noise correction result might be wrong.\n') # get the auxiliary calibration file resourceList = self.manifestXML.getElementsByTagName('resource') resourceList += self.manifestXML.getElementsByTagName('safe:resource') @@ -154,9 +153,52 @@ def get_noise_range_vectors(self, polarization): n[n == 0] = np.nan noise.append(n) pixel.append(np.array(pix)) + + if self.IPFversion <= 2.43: + noise = [np.zeros_like(i) for i in noise] return line, pixel, noise + + def get_swath_id_vectors(self, polarization, line, pixel): + swath_indices = [np.zeros(p.shape, int) for p in pixel] + swathBounds = self.import_swathBounds(polarization) + for iswath, swath_name in enumerate(swathBounds): + swathBound = swathBounds[swath_name] + zipped = zip( + swathBound['firstAzimuthLine'], + swathBound['lastAzimuthLine'], + swathBound['firstRangeSample'], + swathBound['lastRangeSample'], + ) + for fal, lal, frs, lrs in zipped: + valid1 = np.where((line >= fal) * (line <= lal))[0] + for v1 in valid1: + valid2 = (pixel[v1] >= frs) * (pixel[v1] <= lrs) + swath_indices[v1][valid2] = iswath + 1 + return swath_indices + + def get_apg_vectors(self, polarization, line, pixel, min_size=3): + """ Compute Antenna Pattern Gain APG=(1/EAP/RSL)**2 for each noise vectors """ + apg_vectors = [np.zeros(p.size) + np.nan for p in pixel] + swath_indices = self.get_swath_id_vectors(polarization, line, pixel) + + for swid in self.swath_ids: + swath_name = f'{self.obsMode}{swid}' + eap_interpolator = self.get_eap_interpolator(swath_name, polarization) + ba_interpolator = self.get_boresight_angle_interpolator(polarization) + rsl_interpolator = self.get_range_spread_loss_interpolator(polarization) + for i, (l, p, swids) in enumerate(zip(line, pixel, swath_indices)): + gpi = np.where(swids == swid)[0] + if gpi.size > min_size: + ba = ba_interpolator(l, p[gpi]).flatten() + eap = eap_interpolator(ba).flatten() + rsl = rsl_interpolator(l, p[gpi]).flatten() + apg = (1/eap/rsl)**2 + apg_vectors[i][gpi] = apg + + return apg_vectors + def get_swath_interpolator(self, polarization, swath_name, line, pixel, z): """ Prepare interpolators for one swath """ swathBounds = self.import_swathBounds(polarization) @@ -188,38 +230,29 @@ def get_swath_interpolator(self, polarization, swath_name, line, pixel, z): # interpolator for one subswath z_interp2 = RectBivariateSpline(swath_lines, pix_vec_fr, np.array(z_vecs)) return z_interp2, swath_coords - - def get_calibration_vectors(self, polarization, line, pixel): - """ Interpolate sigma0 calibration from XML file to the input line/pixel coordinates """ + + def get_calibration_vectors(self, polarization, line, pixel, name='sigmaNought'): swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] calibrationVector = self.import_calibrationVector(polarization) s0line = np.array(calibrationVector['line']) s0pixel = np.array(calibrationVector['pixel']) - sigma0 = np.array(calibrationVector['sigmaNought']) + sigma0 = np.array(calibrationVector[name]) + + sigma0_vecs = [np.zeros_like(p_vec) + np.nan for p_vec in pixel] - swath_interpolators = [] for swath_name in swath_names: - z_interp2, swath_coords = self.get_swath_interpolator( - polarization, swath_name, s0line, s0pixel, sigma0) - swath_interpolators.append(z_interp2) - - cal = [] - for l, p in zip(line, pixel): - cal_line = np.zeros(p.size) + np.nan - pix_brd = p[np.where(np.diff(p) == 1)[0]] - for swid in range(len(self.swath_ids)): - if swid == list(self.swath_ids)[0]-1: - frs = 0 - else: - frs = pix_brd[swid-1]+1 - if swid == list(self.swath_ids)[-1]-1: - lrs = p[-1] - else: - lrs = pix_brd[swid] - pixel_gpi = (p >= frs) * (p <= lrs) - cal_line[pixel_gpi] = swath_interpolators[swid](l, p[pixel_gpi]).flatten() - cal.append(cal_line) - return cal + sigma0interp, swath_coords = self.get_swath_interpolator(polarization, swath_name, s0line, s0pixel, sigma0) + + for fal, lal, frs, lrs in zip(*swath_coords): + valid1 = np.where((line >= fal) * (line <= lal))[0] + if valid1.size == 0: + continue + for v1 in valid1: + valid2 = np.where( + (pixel[v1] >= frs) * + (pixel[v1] <= lrs))[0] + sigma0_vecs[v1][valid2] = sigma0interp(line[v1], pixel[v1][valid2]) + return sigma0_vecs def get_noise_azimuth_vectors(self, polarization, line, pixel): """ Interpolate scalloping noise from XML files to input pixel/lines coords """ @@ -263,12 +296,19 @@ def get_eap_interpolator(self, subswathID, polarization): elevationAntennaPatternLUT = self.import_elevationAntennaPattern(polarization) eap_lut = np.array(elevationAntennaPatternLUT[subswathID]['elevationAntennaPattern']) eai_lut = elevationAntennaPatternLUT[subswathID]['elevationAngleIncrement'] - recordLength = len(eap_lut)/2 - angleLUT = np.arange(-(recordLength//2),+(recordLength//2)+1) * eai_lut - amplitudeLUT = np.sqrt(eap_lut[0::2]**2 + eap_lut[1::2]**2) + if self.IPFversion > 2.36: + # in case if both real and imaginary parts of EAP are given + recordLength = len(eap_lut)/2 + amplitudeLUT = np.sqrt(eap_lut[0:-1:2]**2 + eap_lut[1::2]**2) + else: + recordLength = len(eap_lut) + # in case if elevationAntennaPattern is given in dB + amplitudeLUT = 10**(eap_lut/10) + + angleLUT = np.arange(-(recordLength//2),+(recordLength//2)+1) * eai_lut eap_interpolator = InterpolatedUnivariateSpline(angleLUT, np.sqrt(amplitudeLUT)) return eap_interpolator - + def get_boresight_angle_interpolator(self, polarization): """ Prepare interpolator for boresaight angles. @@ -294,10 +334,8 @@ def get_boresight_angle_interpolator(self, polarization): rollAngle.append(antennaPattern[subswathID]['roll']) relativeAzimuthTime = np.hstack(relativeAzimuthTime) rollAngle = np.hstack(rollAngle) - rollAngleIntp = InterpolatedUnivariateSpline( - relativeAzimuthTime[sortIndex], rollAngle[sortIndex]) - azimuthTime = [ (t-self.time_coverage_center).total_seconds() - for t in geolocationGridPoint['azimuthTime'] ] + rollAngleIntp = InterpolatedUnivariateSpline(relativeAzimuthTime[sortIndex], rollAngle[sortIndex]) + azimuthTime = [ (t-self.time_coverage_center).total_seconds() for t in geolocationGridPoint['azimuthTime'] ] azimuthTime = np.reshape(azimuthTime, (len(yggp), len(xggp))) roll_map = rollAngleIntp(azimuthTime) @@ -975,7 +1013,7 @@ def import_geolocationGridPoint(self, polarization): get_DOM_nodeValue(iList,[k],'float')) return geolocationGridPoint - def import_antennaPattern(self, polarization): + def import_antennaPattern(self, polarization, teta_ref=29.45): ''' Import antenna pattern from annotation XML DOM ''' antennaPatternList = self.annotationXML[polarization].getElementsByTagName('antennaPattern') antennaPatternList = antennaPatternList[1:] @@ -994,9 +1032,11 @@ def import_antennaPattern(self, polarization): if k=='azimuthTime': antennaPattern[swath][k].append( datetime.strptime(get_DOM_nodeValue(iList,[k]),'%Y-%m-%dT%H:%M:%S.%f')) + elif k == 'roll' and self.IPFversion <= 2.36: + # Sentinel-1-Level-1-Detailed-Algorithm-Definition.pdf, p. 9-12, eq. 9-19 + antennaPattern[swath][k].append(teta_ref) else: - antennaPattern[swath][k].append( - get_DOM_nodeValue(iList,[k],'float')) + antennaPattern[swath][k].append(get_DOM_nodeValue(iList,[k],'float')) return antennaPattern def import_denoisingCoefficients(self, polarization, load_extra_scaling=False): From d0abf6ec29296264633f1a65cd42f142f4b2af8a Mon Sep 17 00:00:00 2001 From: akorosov Date: Mon, 2 Oct 2023 10:18:32 +0200 Subject: [PATCH 02/25] add remove_thermal_noise: algorithm == NERSC_APG --- s1denoise/denoising_parameters.json | 2 +- s1denoise/sentinel1image.py | 167 +++++++++++++++++++++------- s1denoise/utils.py | 63 ++++++++++- 3 files changed, 189 insertions(+), 43 deletions(-) diff --git a/s1denoise/denoising_parameters.json b/s1denoise/denoising_parameters.json index c165eb8..a3d4723 100644 --- a/s1denoise/denoising_parameters.json +++ b/s1denoise/denoising_parameters.json @@ -1 +1 @@ -{"S1A_EW_GRDM_HV_NS_2.4": {"EW1": 1.2226739798419997, "EW2": 0.9615959470712395, "EW3": 1.0292391382243975, "EW4": 1.0065355608166793, "EW5": 0.9218665616511147}, "S1A_EW_GRDM_HV_NS_2.5": {"EW1": 1.2171388595679526, "EW2": 0.9536877350555699, "EW3": 1.015597864698578, "EW4": 0.9970741241853454, "EW5": 0.92307724873829}, "S1A_EW_GRDM_HV_NS_2.6": {"EW1": 1.1719745307602576, "EW2": 0.9163712571049173, "EW3": 0.9681532184092376, "EW4": 0.9430584692825155, "EW5": 0.8780597521669482}, "S1A_EW_GRDM_HV_NS_2.7": {"EW1": 1.398936536855681, "EW2": 0.9814810558391361, "EW3": 1.0515762397929336, "EW4": 1.0190944087059224, "EW5": 0.9595736050922239}, "S1A_EW_GRDM_HV_NS_2.8": {"EW1": 1.3043153896695185, "EW2": 0.9763390733024808, "EW3": 0.9975155379952487, "EW4": 0.9348512736439629, "EW5": 0.9625411730186109}, "S1A_EW_GRDM_HV_NS_2.9": {"EW1": 1.4807347002871454, "EW2": 1.0029777509123536, "EW3": 0.9874088104539668, "EW4": 1.057951324021166, "EW5": 1.0227437274453466}, "S1A_EW_GRDM_HV_PB_2.4": {"EW1": 7.830272905064692e-07, "EW2": 4.449220497638743e-06, "EW3": 4.718034189102612e-05, "EW4": 5.477365452456807e-06, "EW5": -3.1348551872009446e-06}, "S1A_EW_GRDM_HV_PB_2.5": {"EW1": 4.3866672637582335e-05, "EW2": 1.0796195201333851e-05, "EW3": 4.9065318157630286e-05, "EW4": 1.3722300950028815e-05, "EW5": -2.2557295998616656e-06}, "S1A_EW_GRDM_HV_PB_2.6": {"EW1": -3.819198068783059e-05, "EW2": 1.2193826271037744e-05, "EW3": 6.131842637362156e-05, "EW4": 7.392873409112185e-05, "EW5": 6.972248890083653e-05}, "S1A_EW_GRDM_HV_PB_2.7": {"EW1": -3.5621969376463706e-05, "EW2": -3.28432147836913e-05, "EW3": 9.087565093756112e-06, "EW4": 4.712064397346431e-06, "EW5": -6.272081842249038e-07}, "S1A_EW_GRDM_HV_PB_2.8": {"EW1": 0.00010123392382994249, "EW2": 1.4530929340856857e-05, "EW3": 3.552500456185288e-05, "EW4": 1.35067592097569e-05, "EW5": 2.6700987639279118e-05}, "S1A_EW_GRDM_HV_PB_2.9": 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0.00012981706204805575, "IW3": -8.852524353043158e-05}, "S1B_IW_GRDH_VH_NS_3.3": {"IW1": 0.9772665222298795, "IW2": 0.9879026374397193, "IW3": 1.1114295393293259}, "S1B_IW_GRDH_VH_PB_3.3": {"IW1": 9.02727449844173e-05, "IW2": 1.2847170157446009e-05, "IW3": -0.00011160192982708678}, "S1A_EW_GRDM_1SDH_APG_2.36": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.2780833486911206, 0.6447153748822887, 0.06120010862651837, 0.5185029270925529, 0.25458759365322703, 0.441931592130836, 0.5769166974081066, 0.3187637210727542, 0.7161582209673241, 0.2832066859079007, 1.8869459693463089, -0.05307246868379634], "RMSD": 0.2173301323459867}, "S1A_EW_GRDM_1SDH_APG_2.43": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.21601991033670223, 0.8200594281340998, 0.037060834001595544, 0.6158427528571211, 0.030357812386716237, 0.5208016308070372, 0.15788251420999605, 0.5266688199085272, 0.1933608192030616, 0.3972670102439144, 0.6346818901380693, -0.015293589604990832], "RMSD": 0.17746209049311915}, 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[0.26478505122830737, 0.6895598499422747, 0.0503511460882643, 0.42645879271915343, -0.08706296626021108, 0.36734207521810325, -0.1150033414598009, 0.35120706800242135, -0.15975563262997905, 0.27746113031022146, -0.04668574303341855, 0.0075322865475351275], "RMSD": 0.19280759161936628}, "S1B_EW_GRDM_1SDH_APG_3.10": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.2443892336963829, 0.6860614917665326, 0.03849278340641671, 0.4202940482857363, -0.11228356440610353, 0.3725191662411416, -0.12421924309013627, 0.350262659336004, -0.15363719871902293, 0.26575012411307103, -0.10725798911246477, 0.00904053567321228], "RMSD": 0.18443306245961263}, "S1B_EW_GRDM_1SDH_APG_3.20": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.3048306963940611, 0.6931067396756736, 0.10624146857780853, 0.4172911390922358, -0.01798403326978246, 0.36892339467307395, -0.05056070875659868, 0.36609566017218487, -0.051473839641812506, 0.29459072909826983, 0.29105358330367426, -0.003775499831992324], "RMSD": 0.08836301043559097}, "S1B_EW_GRDM_1SDH_APG_3.31": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.25067344336385733, 0.7015344687097671, 0.0523599636808193, 0.4237546153366234, -0.1067508537214939, 0.3721362851174268, -0.1191936001555356, 0.35114326844754296, -0.1555274767234456, 0.2689935372986856, -0.07843852355579913, 0.008682961569285275], "RMSD": 0.19386306893061273}, "S1A_EW_GRDM_1SDH_APG_2.45": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.2286869141257457, 0.8534049703045774, 0.014031085756808781, 0.7116584879760993, 0.1038416774393183, 0.5410269310578825, 0.16302103579137034, 0.6169719318537809, 0.24107788717043888, 0.44495484277952274, 0.7506586002836789, -0.01933497462840117], "RMSD": 0.14119089439843227}, "S1A_EW_GRDM_1SDH_APG_2.72": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.17333879456697707, 0.9359561463813704, 0.008196790501075135, 0.6836937932302471, 0.05364407498623458, 0.52916931097226, 0.0969804326019158, 0.5622228669631257, 0.11066659170734297, 0.40303901051193103, 0.44282668436354516, -0.011090100497155864], "RMSD": 0.13748498948205237}, "S1A_EW_GRDM_1SDH_APG_2.84": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.12976012004950935, 0.8691173152942318, 0.08690426777358995, 0.5899577164882263, -0.04814164224435584, 0.5046433744511001, 0.050919440312370674, 0.49051694750195823, 0.06556121136651263, 0.40653657676066957, 0.28500339725762447, -0.005018437754848226], "RMSD": 0.1313915696330303}, "S1B_EW_GRDM_1SDH_APG_2.84": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.2134156187160474, 0.7542892127132405, 0.10854521717075072, 0.4405823065659708, -0.04698386695329057, 0.3874330726687297, -0.0310880327705667, 0.37338983158575395, -0.10349395893098422, 0.3226215209712821, 0.14039497723195746, 0.00147816844452342], "RMSD": 0.20969661245206334}} \ No newline at end of file diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index a22a7c4..c3cc511 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -3,6 +3,7 @@ import json import os import requests +import shutil import subprocess import xml.etree.ElementTree as ET from xml.dom.minidom import parse, parseString @@ -16,7 +17,12 @@ from scipy.interpolate import interp1d from scipy.ndimage import gaussian_filter -from s1denoise.utils import (cost, fit_noise_scaling_coeff, get_DOM_nodeValue, fill_gaps) +from s1denoise.utils import ( + cost, + fit_noise_scaling_coeff, + get_DOM_nodeValue, + fill_gaps, +) SPEED_OF_LIGHT = 299792458. @@ -24,9 +30,9 @@ class Sentinel1Image(Nansat): """ Cal/Val routines for Sentinel-1 performed on range noise vector coordinatess""" - def __init__(self, filename, mapperName='sentinel1_l1', logLevel=30): + def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): ''' Read calibration/annotation XML files and auxiliary XML file ''' - Nansat.__init__( self, filename, mapperName=mapperName, logLevel=logLevel) + Nansat.__init__(self, filename, mapper=mapper, log_level=log_level, fast=fast) if ( self.filename.split(os.sep)[-1][4:16] not in [ 'IW_GRDH_1SDH', 'IW_GRDH_1SDV', @@ -95,11 +101,11 @@ def __init__(self, filename, mapperName='sentinel1_l1', logLevel=30): self.IPFversion = float(self.manifestXML.getElementsByTagName('safe:software')[0] .attributes['version'].value) if self.IPFversion < 2.43: - print('\nERROR: IPF version of input image is lower than 2.43! \n' - 'Denoising vectors in annotation files are not qualified.\n' - 'Only G_TOT-based denoising can be performed\n') + print('\nERROR: IPF version of input image is lower than 2.43! ' + 'Denoising vectors in annotation files are not qualified. ' + 'Only APG-based denoising can be performed\n') elif 2.43 <= self.IPFversion < 2.53: - print('\nWARNING: IPF version of input image is lower than 2.53! \n' + print('\nWARNING: IPF version of input image is lower than 2.53! ' 'ESA default noise correction result might be wrong.\n') # get the auxiliary calibration file resourceList = self.manifestXML.getElementsByTagName('resource') @@ -127,19 +133,31 @@ def download_aux_calibration(self, filename, platform): cd = filename.split('_')[4].lstrip('G') creation_date = f'{cd[:4]}-{cd[4:6]}-{cd[6:8]}T{cd[9:11]}:{cd[11:13]}:{cd[13:15]}' - api_url = f'https://sar-mpc.eu/api/v1/?product_type=AUX_CAL&validity_start={validity_start}&creation_date={creation_date}' - with requests.get(api_url, stream=True) as r: - uuid = json.loads(r.content.decode())['results'][0]['uuid'] - - download_file = os.path.join(self.aux_data_dir, filename + '.zip') - aux_cal_url = f'https://sar-mpc.eu/download/{uuid}/' - print(f'downloading {filename}.zip from {aux_cal_url}') - with requests.get(aux_cal_url, stream=True) as r: + def get_remote_url(api_url): + with requests.get(api_url, stream=True) as r: + rjson = json.loads(r.content.decode()) + remote_url = rjson['results'][0]['remote_url'] + physical_name = rjson['results'][0]['physical_name'] + return remote_url, physical_name + + try: + remote_url, physical_name = get_remote_url(f'https://sar-mpc.eu/api/v1/?product_type=AUX_CAL&validity_start={validity_start}&creation_date={creation_date}') + except: + remote_url, physical_name = get_remote_url(f'https://sar-mpc.eu/api/v1/?product_type=AUX_CAL&validity_start={validity_start}') + + download_file = os.path.join(self.aux_data_dir, physical_name) + print(f'downloading {filename}.zip from {remote_url}') + with requests.get(remote_url, stream=True) as r: with open(download_file, "wb") as f: f.write(r.content) with zipfile.ZipFile(download_file, 'r') as download_zip: download_zip.extractall(path=self.aux_data_dir) + output_dir = f'{self.aux_data_dir}/{filename}' + if not os.path.exists(output_dir): + # in case the physical_name of the downloaded file does not exactly match the filename from manifest + # the best found AUX-CAL file is copied to the filename from manifest + shutil.copytree(download_file.rstrip('.zip'), output_dir) def get_noise_range_vectors(self, polarization): """ Get range noise from XML files and return noise, pixels and lines for non-zero elems""" @@ -198,6 +216,33 @@ def get_apg_vectors(self, polarization, line, pixel, min_size=3): apg_vectors[i][gpi] = apg return apg_vectors + + def get_apg_scales_offsets(self): + params = self.load_denoising_parameters_json() + apg_id = f'{os.path.basename(self.filename)[:16]}_APG_{self.IPFversion:04.2f}' + p = params[apg_id] + offsets = [] + scales = [] + for i in range(5): + offsets.append(p['B'][i*2] / p['Y_SCALE']) + scales.append(p['B'][1+i*2] * p['A_SCALE'] / p['Y_SCALE']) + return scales, offsets + + def get_apg_based_noise_vectors(self, polarization, line, pixel): + scales, offsets = self.get_apg_scales_offsets() + cal_s0hv = self.get_calibration_vectors(polarization, line, pixel) + scall_hv = self.get_noise_azimuth_vectors(polarization, line, pixel) + swath_ids = self.get_swath_id_vectors(polarization, line, pixel) + apg = self.get_apg_vectors(polarization, line, pixel) + apg = self.calibrate_noise_vectors(apg, cal_s0hv, scall_hv) + + nesz_apg = [np.zeros_like(i) for i in apg] + for i in range(len(apg)): + for j in range(1,6): + gpi = swath_ids[i] == j + nesz_apg[i][gpi] = offsets[j-1] + apg[i][gpi] * scales[j-1] + + return nesz_apg def get_swath_interpolator(self, polarization, swath_name, line, pixel, z): """ Prepare interpolators for one swath """ @@ -231,6 +276,21 @@ def get_swath_interpolator(self, polarization, swath_name, line, pixel, z): z_interp2 = RectBivariateSpline(swath_lines, pix_vec_fr, np.array(z_vecs)) return z_interp2, swath_coords + def get_elevation_incidence_angle_interpolators(self, polarization): + geolocationGridPoint = self.import_geolocationGridPoint(polarization) + xggp = np.unique(geolocationGridPoint['pixel']) + yggp = np.unique(geolocationGridPoint['line']) + elevation_map = np.array(geolocationGridPoint['elevationAngle']).reshape(len(yggp), len(xggp)) + incidence_map = np.array(geolocationGridPoint['incidenceAngle']).reshape(len(yggp), len(xggp)) + elevation_angle_interpolator = RectBivariateSpline(yggp, xggp, elevation_map) + incidence_angle_interpolator = RectBivariateSpline(yggp, xggp, incidence_map) + return elevation_angle_interpolator, incidence_angle_interpolator + + def get_elevation_incidence_angle_vectors(self, ea_interpolator, ia_interpolator, line, pixel): + ea = [ea_interpolator(l, p).flatten() for (l, p) in zip(line, pixel)] + ia = [ia_interpolator(l, p).flatten() for (l, p) in zip(line, pixel)] + return ea, ia + def get_calibration_vectors(self, polarization, line, pixel, name='sigmaNought'): swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] calibrationVector = self.import_calibrationVector(polarization) @@ -295,15 +355,13 @@ def get_eap_interpolator(self, subswathID, polarization): """ elevationAntennaPatternLUT = self.import_elevationAntennaPattern(polarization) eap_lut = np.array(elevationAntennaPatternLUT[subswathID]['elevationAntennaPattern']) + recordLength = elevationAntennaPatternLUT[subswathID]['elevationAntennaPatternCount'] eai_lut = elevationAntennaPatternLUT[subswathID]['elevationAngleIncrement'] - if self.IPFversion > 2.36: - # in case if both real and imaginary parts of EAP are given - recordLength = len(eap_lut)/2 - amplitudeLUT = np.sqrt(eap_lut[0:-1:2]**2 + eap_lut[1::2]**2) - else: - recordLength = len(eap_lut) + if recordLength == eap_lut.shape[0]: # in case if elevationAntennaPattern is given in dB amplitudeLUT = 10**(eap_lut/10) + else: + amplitudeLUT = np.sqrt(eap_lut[0:-1:2]**2 + eap_lut[1::2]**2) angleLUT = np.arange(-(recordLength//2),+(recordLength//2)+1) * eai_lut eap_interpolator = InterpolatedUnivariateSpline(angleLUT, np.sqrt(amplitudeLUT)) @@ -805,7 +863,29 @@ def get_corrected_nesz_full_size(self, polarization, nesz): nesz_corrected[fal:lal+1, frs:lrs+1] += pb[swath_name] return nesz_corrected - def get_raw_sigma0_full_size(self, polarization): + def get_nesz_apg_full_size(self, polarization): + line, pixel, noise = self.get_noise_range_vectors(polarization) + nesz_apg = self.get_apg_based_noise_vectors(polarization, line, pixel) + scales, offsets = self.get_apg_scales_offsets() + apg = self.get_apg_vectors(polarization, line, pixel) + swath_ids = self.get_swath_id_vectors(polarization, line, pixel) + + nesz_apg = [np.zeros_like(i) for i in apg] + for i in range(len(apg)): + for j in range(1,6): + gpi = swath_ids[i] == j + nesz_apg[i][gpi] = offsets[j-1] + apg[i][gpi] * scales[j-1] + + scal_fs = self.get_scalloping_full_size(polarization) + cal_fs = self.get_calibration_full_size(polarization, power=2) + nesz_apg_fs = self.interp_nrv_full_size(nesz_apg, line, pixel, polarization, power=1) + nesz_apg_fs *= scal_fs + nesz_apg_fs /= cal_fs + + return nesz_apg_fs + + + def get_raw_sigma0_full_size(self, polarization, min_dn=0): """ Read DN from input GeoTiff file and calibrate """ src_filename = self.bands()[self.get_band_number(f'DN_{polarization}')]['SourceFilename'] ds = gdal.Open(src_filename) @@ -815,7 +895,7 @@ def get_raw_sigma0_full_size(self, polarization): cal_s0 = self.get_calibration_vectors(polarization, line, pixel) sigma0_fs = dn.astype(float)**2 / self.get_calibration_full_size(polarization, power=2) - sigma0_fs[dn == 0] = np.nan + sigma0_fs[dn <= min_dn] = np.nan return sigma0_fs @@ -836,16 +916,17 @@ def export_noise_xml(self, polarization, output_path): tree.write(os.path.join(output_path, os.path.basename(crosspol_noise_file))) return crosspol_noise_file - def remove_thermal_noise(self, polarization, algorithm='NERSC', remove_negative=True): + def remove_thermal_noise(self, polarization, algorithm='NERSC', remove_negative=True, min_dn=0): """ Get full size matrix with sigma0 - NESZ """ - sigma0 = self.get_raw_sigma0_full_size(polarization) - sigma0[sigma0 == 0] = np.nan if algorithm == 'NERSC': nesz = self.get_nesz_full_size(polarization, shift_lut=True) nesz = self.get_corrected_nesz_full_size(polarization, nesz) + elif algorithm == 'NERSC_APG': + nesz = self.get_nesz_apg_full_size(polarization) else: nesz = self.get_nesz_full_size(polarization) - + + sigma0 = self.get_raw_sigma0_full_size(polarization, min_dn=min_dn) sigma0 -= nesz if remove_negative: @@ -968,7 +1049,9 @@ def import_elevationAntennaPattern(self, polarization): { 'elevationAngleIncrement':[], 'elevationAntennaPattern':[], 'absoluteCalibrationConstant':[], - 'noiseCalibrationFactor':[] } + 'noiseCalibrationFactor':[], + 'elevationAntennaPatternCount': None + } for li in self.swath_ids } for iList in calParamsList: swath = get_DOM_nodeValue(iList,['swath']) @@ -977,14 +1060,12 @@ def import_elevationAntennaPattern(self, polarization): elem = iList.getElementsByTagName('elevationAntennaPattern')[0] for k in elevationAntennaPattern[swath].keys(): if k=='elevationAngleIncrement': - elevationAntennaPattern[swath][k] = ( - get_DOM_nodeValue(elem,[k],'float') ) + elevationAntennaPattern[swath][k] = get_DOM_nodeValue(elem,[k],'float') elif k=='elevationAntennaPattern': - elevationAntennaPattern[swath][k] = ( - get_DOM_nodeValue(elem,['values'],'float') ) - else: - elevationAntennaPattern[swath][k] = ( - get_DOM_nodeValue(iList,[k],'float') ) + elevationAntennaPattern[swath][k] = get_DOM_nodeValue(elem,['values'],'float') + elevationAntennaPattern[swath]['elevationAntennaPatternCount'] = int(elem.getElementsByTagName('values')[0].getAttribute('count')) + elif k in ['absoluteCalibrationConstant', 'noiseCalibrationFactor']: + elevationAntennaPattern[swath][k] = get_DOM_nodeValue(iList,[k],'float') return elevationAntennaPattern def import_geolocationGridPoint(self, polarization): @@ -1039,17 +1120,21 @@ def import_antennaPattern(self, polarization, teta_ref=29.45): antennaPattern[swath][k].append(get_DOM_nodeValue(iList,[k],'float')) return antennaPattern + def load_denoising_parameters_json(self): + denoise_filename = os.path.join( + os.path.dirname(os.path.realpath(__file__)), + 'denoising_parameters.json') + with open(denoise_filename) as f: + params = json.load(f) + return params + def import_denoisingCoefficients(self, polarization, load_extra_scaling=False): ''' Import denoising coefficients ''' filename_parts = os.path.basename(self.filename).split('_') platform = filename_parts[0] mode = filename_parts[1] resolution = filename_parts[2] - denoise_filename = os.path.join( - os.path.dirname(os.path.realpath(__file__)), - 'denoising_parameters.json') - with open(denoise_filename) as f: - params = json.load(f) + params = self.load_denoising_parameters_json() noiseScalingParameters = {} powerBalancingParameters = {} @@ -1109,7 +1194,7 @@ def remove_texture_noise(self, polarization, window=3, weight=0.15, s0_min=0, re Parameters ---------- - polarisation : str + polarization : str 'HH' or 'HV' window : int Size of window in the gaussian filter diff --git a/s1denoise/utils.py b/s1denoise/utils.py index f1a7f27..8579a75 100644 --- a/s1denoise/utils.py +++ b/s1denoise/utils.py @@ -1,6 +1,7 @@ +from collections import defaultdict + import numpy as np from scipy.stats import pearsonr -from scipy.ndimage import minimum_filter from scipy.optimize import fminbound from scipy.ndimage.morphology import distance_transform_edt @@ -80,3 +81,63 @@ def fill_gaps(array, mask, distance=15): r,c = indi[:,gpi] array[gpi] = array[r,c] return array + +def skip_swath_borders(swath_ids, skip=1): + swath_ids_skip = [] + for swid in swath_ids: + swid_skip = np.array(swid) + for j in range(1,6): + gpi = np.where(swid == j)[0] + if gpi.size > 0: + swid_skip[gpi[:skip]] = 0 + swid_skip[gpi[-skip:]] = 0 + swath_ids_skip.append(swid_skip) + return swath_ids_skip + +def build_AY_matrix(swath_ids, sigma0hv, apg, incang, s0hv_max): + "HV = 1, IA, 1, APG1, 1, APG2, 1, APG3, 1, APG4, 1, APG5" + + A_123 = [] # [1] + A_apg = defaultdict(list) # [APG scale and 1] + Y = [] # [HV] + + for iswath in range(1,6): + for swath_ids_v, sigma0hv_v, apg_v, incang_v in zip(swath_ids, sigma0hv, apg, incang): + gpi = np.where(swath_ids_v == iswath)[0] + # append only small enough values of HV + if np.nanmean(sigma0hv_v[gpi]) < s0hv_max[iswath]: + A_123.append([ + np.ones(gpi.size), + incang_v[gpi], + ]) + A_apg[iswath].append(np.vstack([ + np.ones(gpi.size), + apg_v[gpi], + ]).T) + Y.append(sigma0hv_v[gpi]) + + if len(A_123) == 0: + return None, None + A_123 = np.hstack(A_123).T + Y = np.hstack(Y)[None].T + + A = [] + for iswath in A_apg: + A_apg_stack = np.vstack(A_apg[iswath]) + A_apg_all = np.zeros((A_apg_stack.shape[0], 10)) + A_apg_all[:, int((iswath-1)*2):int((iswath-1)*2+2)] = A_apg_stack + A.append(A_apg_all) + + A = np.hstack([np.vstack(A), A_123]) + gpi = np.isfinite(A.sum(axis=1)) * np.isfinite(Y.flat) + + A = A[gpi] + Y = Y[gpi] + + return A, Y + +def solve(A, Y): + B = np.linalg.lstsq(A, Y, rcond=None) + Y_rec = np.dot(A, B[0]) + rmsd = np.sqrt(np.mean((Y - Y_rec)**2)) + return B[0].flatten(), rmsd From e10dfa705aad8b4fd8a2b9ba5c02df37b0e1a4aa Mon Sep 17 00:00:00 2001 From: akorosov Date: Mon, 2 Oct 2023 15:47:50 +0200 Subject: [PATCH 03/25] update get_nesz_full_size, remove_thermal_noise --- s1denoise/denoising_parameters.json | 2 +- s1denoise/sentinel1image.py | 126 +++++++++++----------------- 2 files changed, 49 insertions(+), 79 deletions(-) diff --git a/s1denoise/denoising_parameters.json b/s1denoise/denoising_parameters.json index a3d4723..09d8129 100644 --- a/s1denoise/denoising_parameters.json +++ b/s1denoise/denoising_parameters.json @@ -1 +1 @@ -{"S1A_EW_GRDM_HV_NS_2.4": {"EW1": 1.2226739798419997, "EW2": 0.9615959470712395, "EW3": 1.0292391382243975, "EW4": 1.0065355608166793, "EW5": 0.9218665616511147}, "S1A_EW_GRDM_HV_NS_2.5": {"EW1": 1.2171388595679526, "EW2": 0.9536877350555699, "EW3": 1.015597864698578, "EW4": 0.9970741241853454, "EW5": 0.92307724873829}, "S1A_EW_GRDM_HV_NS_2.6": {"EW1": 1.1719745307602576, "EW2": 0.9163712571049173, "EW3": 0.9681532184092376, "EW4": 0.9430584692825155, "EW5": 0.8780597521669482}, "S1A_EW_GRDM_HV_NS_2.7": {"EW1": 1.398936536855681, "EW2": 0.9814810558391361, "EW3": 1.0515762397929336, "EW4": 1.0190944087059224, "EW5": 0.9595736050922239}, "S1A_EW_GRDM_HV_NS_2.8": {"EW1": 1.3043153896695185, "EW2": 0.9763390733024808, "EW3": 0.9975155379952487, "EW4": 0.9348512736439629, "EW5": 0.9625411730186109}, "S1A_EW_GRDM_HV_NS_2.9": {"EW1": 1.4807347002871454, "EW2": 1.0029777509123536, "EW3": 0.9874088104539668, "EW4": 1.057951324021166, "EW5": 1.0227437274453466}, "S1A_EW_GRDM_HV_PB_2.4": {"EW1": 7.830272905064692e-07, "EW2": 4.449220497638743e-06, "EW3": 4.718034189102612e-05, "EW4": 5.477365452456807e-06, "EW5": -3.1348551872009446e-06}, "S1A_EW_GRDM_HV_PB_2.5": {"EW1": 4.3866672637582335e-05, "EW2": 1.0796195201333851e-05, "EW3": 4.9065318157630286e-05, "EW4": 1.3722300950028815e-05, "EW5": -2.2557295998616656e-06}, "S1A_EW_GRDM_HV_PB_2.6": {"EW1": -3.819198068783059e-05, "EW2": 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"S1A_EW_GRDM_1SDH_APG_2.84": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.0, -6.591949208711867e-16, 0.23720348202083294, 0.520836167653954, -0.04408890502122296, 0.49706197437168737, 0.14739350527622214, 0.45949997881401694, 0.13423990279303216, 0.3753827504690209, 0.4747479850688619, -0.010099172888269455], "RMSD": 0.11822762933744052}, "S1B_EW_GRDM_1SDH_APG_2.84": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.200729639474916, 0.7566253787790845, 0.09564844642668935, 0.45221384831110234, -0.025224975273230817, 0.38845292335461046, -0.009647184589825636, 0.37942209860373954, -0.05862426871166229, 0.3200344261355591, 0.2028816573268845, -0.0012446502601640708], "RMSD": 0.20740461875525806}} \ No newline at end of file diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index c3cc511..fb3a5a0 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -212,7 +212,7 @@ def get_apg_vectors(self, polarization, line, pixel, min_size=3): ba = ba_interpolator(l, p[gpi]).flatten() eap = eap_interpolator(ba).flatten() rsl = rsl_interpolator(l, p[gpi]).flatten() - apg = (1/eap/rsl)**2 + apg = (1 / eap / rsl) ** 2 apg_vectors[i][gpi] = apg return apg_vectors @@ -228,22 +228,6 @@ def get_apg_scales_offsets(self): scales.append(p['B'][1+i*2] * p['A_SCALE'] / p['Y_SCALE']) return scales, offsets - def get_apg_based_noise_vectors(self, polarization, line, pixel): - scales, offsets = self.get_apg_scales_offsets() - cal_s0hv = self.get_calibration_vectors(polarization, line, pixel) - scall_hv = self.get_noise_azimuth_vectors(polarization, line, pixel) - swath_ids = self.get_swath_id_vectors(polarization, line, pixel) - apg = self.get_apg_vectors(polarization, line, pixel) - apg = self.calibrate_noise_vectors(apg, cal_s0hv, scall_hv) - - nesz_apg = [np.zeros_like(i) for i in apg] - for i in range(len(apg)): - for j in range(1,6): - gpi = swath_ids[i] == j - nesz_apg[i][gpi] = offsets[j-1] + apg[i][gpi] * scales[j-1] - - return nesz_apg - def get_swath_interpolator(self, polarization, swath_name, line, pixel, z): """ Prepare interpolators for one swath """ swathBounds = self.import_swathBounds(polarization) @@ -341,11 +325,7 @@ def get_noise_azimuth_vectors(self, polarization, line, pixel): def calibrate_noise_vectors(self, noise, cal_s0, scall): """ Compute calibrated NESZ from input noise, sigma0 calibration and scalloping noise""" - nesz = [] - for n, cal, scall0 in zip(noise, cal_s0, scall): - n_calib = scall0 * n / cal**2 - nesz.append(n_calib) - return nesz + return [s * n / c**2 for n, c, s in zip(noise, cal_s0, scall)] def get_eap_interpolator(self, subswathID, polarization): """ @@ -455,11 +435,13 @@ def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip = 4): apg = (1/eap/rsp)**2 noise_valid = np.array(noise[v1][valid2]) - noise_interpolator = InterpolatedUnivariateSpline(valid_pix, noise_valid) - pixel_shift = minimize(cost, 0, args=(valid_pix[skip:-skip], noise_interpolator, apg[skip:-skip])).x[0] - - noise_shifted0 = noise_interpolator(valid_pix + pixel_shift) - noise_shifted[v1][valid2] = noise_shifted0 + if np.allclose(noise_valid, noise_valid[0]): + noise_shifted[v1][valid2] = noise_valid + else: + noise_interpolator = InterpolatedUnivariateSpline(valid_pix, noise_valid) + pixel_shift = minimize(cost, 0, args=(valid_pix[skip:-skip], noise_interpolator, apg[skip:-skip])).x[0] + noise_shifted0 = noise_interpolator(valid_pix + pixel_shift) + noise_shifted[v1][valid2] = noise_shifted0 return noise_shifted @@ -829,18 +811,6 @@ def get_calibration_full_size(self, polarization, power=1): s0 = np.array(calibrationVector['sigmaNought']) return self.interp_nrv_full_size(s0, line, pixel, polarization, power=power) - def get_nesz_full_size(self, polarization, shift_lut=False): - """ Get NESZ at full resolution and full size matrix """ - line, pixel, noise = self.get_noise_range_vectors(polarization) - if shift_lut: - noise = self.get_shifted_noise_vectors(polarization, line, pixel, noise) - - nesz_fs = self.interp_nrv_full_size(noise, line, pixel, polarization) - nesz_fs *= self.get_scalloping_full_size(polarization) - nesz_fs /= self.get_calibration_full_size(polarization, power=2) - - return nesz_fs - def get_corrected_nesz_full_size(self, polarization, nesz): """ Get corrected NESZ on full size matrix """ nesz_corrected = np.array(nesz) @@ -863,40 +833,14 @@ def get_corrected_nesz_full_size(self, polarization, nesz): nesz_corrected[fal:lal+1, frs:lrs+1] += pb[swath_name] return nesz_corrected - def get_nesz_apg_full_size(self, polarization): - line, pixel, noise = self.get_noise_range_vectors(polarization) - nesz_apg = self.get_apg_based_noise_vectors(polarization, line, pixel) - scales, offsets = self.get_apg_scales_offsets() - apg = self.get_apg_vectors(polarization, line, pixel) - swath_ids = self.get_swath_id_vectors(polarization, line, pixel) - - nesz_apg = [np.zeros_like(i) for i in apg] - for i in range(len(apg)): - for j in range(1,6): - gpi = swath_ids[i] == j - nesz_apg[i][gpi] = offsets[j-1] + apg[i][gpi] * scales[j-1] - - scal_fs = self.get_scalloping_full_size(polarization) - cal_fs = self.get_calibration_full_size(polarization, power=2) - nesz_apg_fs = self.interp_nrv_full_size(nesz_apg, line, pixel, polarization, power=1) - nesz_apg_fs *= scal_fs - nesz_apg_fs /= cal_fs - - return nesz_apg_fs - - def get_raw_sigma0_full_size(self, polarization, min_dn=0): """ Read DN from input GeoTiff file and calibrate """ src_filename = self.bands()[self.get_band_number(f'DN_{polarization}')]['SourceFilename'] ds = gdal.Open(src_filename) dn = ds.ReadAsArray() - line, pixel, noise = self.get_noise_range_vectors(polarization) - cal_s0 = self.get_calibration_vectors(polarization, line, pixel) - sigma0_fs = dn.astype(float)**2 / self.get_calibration_full_size(polarization, power=2) sigma0_fs[dn <= min_dn] = np.nan - return sigma0_fs def export_noise_xml(self, polarization, output_path): @@ -916,18 +860,42 @@ def export_noise_xml(self, polarization, output_path): tree.write(os.path.join(output_path, os.path.basename(crosspol_noise_file))) return crosspol_noise_file - def remove_thermal_noise(self, polarization, algorithm='NERSC', remove_negative=True, min_dn=0): - """ Get full size matrix with sigma0 - NESZ """ + def get_noise_apg_vectors(self, polarization, line, pixel): + noise = [np.zeros_like(i) for i in pixel] + scales, offsets = self.get_apg_scales_offsets() + apg = self.get_apg_vectors(polarization, line, pixel) + swath_ids = self.get_swath_id_vectors(polarization, line, pixel) + for i in range(len(apg)): + for j in range(1,6): + gpi = swath_ids[i] == j + noise[i][gpi] = offsets[j-1] + apg[i][gpi] * scales[j-1] + return noise + + def get_nesz_full_size(self, polarization, algorithm): + # ESA noise vectors + line, pixel, noise = self.get_noise_range_vectors(polarization) if algorithm == 'NERSC': - nesz = self.get_nesz_full_size(polarization, shift_lut=True) - nesz = self.get_corrected_nesz_full_size(polarization, nesz) + # NERSC correction of noise shift in range direction + noise = self.get_shifted_noise_vectors(polarization, line, pixel, noise) elif algorithm == 'NERSC_APG': - nesz = self.get_nesz_apg_full_size(polarization) - else: - nesz = self.get_nesz_full_size(polarization) - + # APG-based noise vectors + noise = self.get_noise_apg_vectors(polarization, line, pixel) + # noise calibration + cal_s0 = self.get_calibration_vectors(polarization, line, pixel) + nesz = [n / c**2 for (n,c) in zip(noise, cal_s0)] + # noise full size + nesz_fs = self.interp_nrv_full_size(nesz, line, pixel, polarization) + # scalloping correction + nesz_fs *= self.get_scalloping_full_size(polarization) + # NERSC correction of NESZ magnitude + if algorithm == 'NERSC': + nesz_fs = self.get_corrected_nesz_full_size(polarization, nesz_fs) + return nesz_fs + + def remove_thermal_noise(self, polarization, algorithm='NERSC', remove_negative=True, min_dn=0): + nesz_fs = self.get_nesz_full_size(polarization, algorithm) sigma0 = self.get_raw_sigma0_full_size(polarization, min_dn=min_dn) - sigma0 -= nesz + sigma0 -= nesz_fs if remove_negative: sigma0 = fill_gaps(sigma0, sigma0 <= 0) @@ -1157,12 +1125,14 @@ def import_denoisingCoefficients(self, polarization, load_extra_scaling=False): noiseScalingParameters[subswathID] = params[ns_key].get(subswathID, 1) else: print(f'WARNING: noise scaling for {subswathID} (IPF:{IPFversion}) is missing.') + noiseScalingParameters[subswathID] = 1 pb_key = f'{base_key}_PB_%0.1f' % IPFversion if pb_key in params: powerBalancingParameters[subswathID] = params[pb_key].get(subswathID, 0) else: print(f'WARNING: power balancing for {subswathID} (IPF:{IPFversion}) is missing.') + powerBalancingParameters[subswathID] = 0 if not load_extra_scaling: continue @@ -1184,7 +1154,7 @@ def import_denoisingCoefficients(self, polarization, load_extra_scaling=False): return ( noiseScalingParameters, powerBalancingParameters, extraScalingParameters, noiseVarianceParameters ) - def remove_texture_noise(self, polarization, window=3, weight=0.15, s0_min=0, remove_negative=True, **kwargs): + def remove_texture_noise(self, polarization, window=3, weight=0.15, s0_min=0, remove_negative=True, algorithm='NERSC', min_dn=0, **kwargs): """ Thermal noise removal followed by textural noise compensation using Method2 Method2 is implemented as a weighted average of sigma0 and sigma0 smoothed with @@ -1209,11 +1179,11 @@ def remove_texture_noise(self, polarization, window=3, weight=0.15, s0_min=0, re Full size array with thermal and texture noise removed """ + if self.IPFversion == 3.2: self.IPFversion = 3.1 - sigma0 = self.get_raw_sigma0_full_size(polarization) - nesz = self.get_nesz_full_size(polarization, shift_lut=True) - nesz = self.get_corrected_nesz_full_size(polarization, nesz) + sigma0 = self.get_raw_sigma0_full_size(polarization, min_dn=min_dn) + nesz = self.get_nesz_full_size(polarization, algorithm) sigma0 -= nesz s0_offset = np.nanmean(nesz) From 6ae4d1560d39e33328116340b343a86f2f9a4f42 Mon Sep 17 00:00:00 2001 From: akorosov Date: Tue, 3 Oct 2023 15:46:23 +0200 Subject: [PATCH 04/25] add compute_roll, orbitAtGivenTime, import_orbit from JWP --- s1denoise/sentinel1image.py | 81 ++++++++++++++++++++++++++++++------- s1denoise/utils.py | 4 ++ 2 files changed, 71 insertions(+), 14 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index fb3a5a0..a0e13cf 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -22,6 +22,7 @@ fit_noise_scaling_coeff, get_DOM_nodeValue, fill_gaps, + cubic_hermite_interpolation, ) SPEED_OF_LIGHT = 299792458. @@ -1062,32 +1063,84 @@ def import_geolocationGridPoint(self, polarization): get_DOM_nodeValue(iList,[k],'float')) return geolocationGridPoint - def import_antennaPattern(self, polarization, teta_ref=29.45): + def import_antennaPattern(self, polarization): ''' Import antenna pattern from annotation XML DOM ''' + swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] antennaPatternList = self.annotationXML[polarization].getElementsByTagName('antennaPattern') antennaPatternList = antennaPatternList[1:] - antennaPattern = { '%s%s' % (self.obsMode, li): - { 'azimuthTime':[], - 'slantRangeTime':[], - 'elevationAngle':[], - 'elevationPattern':[], - 'incidenceAngle':[], - 'terrainHeight':[], - 'roll':[] } - for li in self.swath_ids } + keys = ['azimuthTime', 'slantRangeTime', 'elevationAngle', 'elevationPattern', 'incidenceAngle', 'terrainHeight', 'roll'] + antennaPattern = {swath_name: {key: [] for key in keys} for swath_name in swath_names} + compute_roll = False for iList in antennaPatternList: - swath = get_DOM_nodeValue(iList,['swath']) + swath = get_DOM_nodeValue(iList, ['swath']) for k in antennaPattern[swath].keys(): if k=='azimuthTime': antennaPattern[swath][k].append( datetime.strptime(get_DOM_nodeValue(iList,[k]),'%Y-%m-%dT%H:%M:%S.%f')) - elif k == 'roll' and self.IPFversion <= 2.36: - # Sentinel-1-Level-1-Detailed-Algorithm-Definition.pdf, p. 9-12, eq. 9-19 - antennaPattern[swath][k].append(teta_ref) + elif k == 'roll' and len(iList.getElementsByTagName('roll')) == 0: + antennaPattern[swath][k].append(None) + compute_roll = True else: antennaPattern[swath][k].append(get_DOM_nodeValue(iList,[k],'float')) + if compute_roll: + for swath_name in swath_names: + antennaPattern[swath_name]['roll'] = self.compute_roll(polarization, antennaPattern[swath_name]) return antennaPattern + def import_orbit(self, polarization): + ''' Import orbit information from annotation XML DOM ''' + orbitList = self.annotationXML[polarization].getElementsByTagName('orbit') + orbit = { 'time':[], + 'position':{'x':[], 'y':[], 'z':[]}, + 'velocity':{'x':[], 'y':[], 'z':[]} } + for iList in orbitList: + orbit['time'].append( + datetime.strptime(get_DOM_nodeValue(iList,['time']), '%Y-%m-%dT%H:%M:%S.%f')) + for k in orbit['position'].keys(): + orbit['position'][k].append( + get_DOM_nodeValue(iList,['position',k],'float')) + for k in orbit['velocity'].keys(): + orbit['velocity'][k].append( + get_DOM_nodeValue(iList,['velocity',k],'float')) + return orbit + + def orbitAtGivenTime(self, polarization, relativeAzimuthTime): + ''' Interpolate orbit parameters for given time vector ''' + stateVectors = self.import_orbit(polarization) + orbitTime = np.array([ (t-self.time_coverage_center).total_seconds() + for t in stateVectors['time'] ]) + orbitAtGivenTime = { 'relativeAzimuthTime':relativeAzimuthTime, + 'positionXYZ':[], + 'velocityXYZ':[] } + for t in relativeAzimuthTime: + useIndices = sorted(np.argsort(abs(orbitTime-t))[:4]) + for k in ['position', 'velocity']: + orbitAtGivenTime[k+'XYZ'].append([ + cubic_hermite_interpolation(orbitTime[useIndices], + np.array(stateVectors[k][component])[useIndices], t) + for component in ['x','y','z'] ]) + for k in ['positionXYZ', 'velocityXYZ']: + orbitAtGivenTime[k] = np.squeeze(orbitAtGivenTime[k]) + return orbitAtGivenTime + + def compute_roll(self, polarization, antenna_pattern): + relativeAzimuthTime = np.array([ + (t - self.time_coverage_center).total_seconds() + for t in antenna_pattern['azimuthTime'] + ]) + positionXYZ = self.orbitAtGivenTime(polarization, relativeAzimuthTime)['positionXYZ'] + satelliteLatitude = np.arctan2(positionXYZ[:,2], np.sqrt(positionXYZ[:,0]**2 + positionXYZ[:,1]**2)) + r_major = 6378137.0 # WGS84 semi-major axis + r_minor = 6356752.314245179 # WGS84 semi-minor axis + earthRadius = np.sqrt( ( (r_major**2 * np.cos(satelliteLatitude))**2 + + (r_minor**2 * np.sin(satelliteLatitude))**2) + / ( (r_major * np.cos(satelliteLatitude))**2 + + (r_minor * np.sin(satelliteLatitude))**2) ) + satelliteAltitude = np.linalg.norm(positionXYZ, axis=1) - earthRadius + # see Eq.9-19 in the reference R2. + rollAngle = 29.45 - 0.0566*(satelliteAltitude/1000. - 711.7) + return rollAngle + def load_denoising_parameters_json(self): denoise_filename = os.path.join( os.path.dirname(os.path.realpath(__file__)), diff --git a/s1denoise/utils.py b/s1denoise/utils.py index 8579a75..c7d728c 100644 --- a/s1denoise/utils.py +++ b/s1denoise/utils.py @@ -5,6 +5,10 @@ from scipy.optimize import fminbound from scipy.ndimage.morphology import distance_transform_edt +def cubic_hermite_interpolation(x,y,xi): + ''' Get interpolated value for given time ''' + return np.polynomial.hermite.hermval(xi, np.polynomial.hermite.hermfit(x,y,deg=3)) + def cost(x, pix_valid, interp, y_ref): """ Cost function for finding noise LUT shift in Range """ y = interp(pix_valid+x) From 0ef1d1dcc4b8e9d73569ae17e0f4fc8fe7716cd4 Mon Sep 17 00:00:00 2001 From: akorosov Date: Tue, 3 Oct 2023 17:00:51 +0200 Subject: [PATCH 05/25] split get_eap_rsl_vectors; update get_raw_sigma0_vectors_from_full_size --- s1denoise/sentinel1image.py | 43 ++++++++++++++++++++++++------------- 1 file changed, 28 insertions(+), 15 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index a0e13cf..cdea2a6 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -197,11 +197,12 @@ def get_swath_id_vectors(self, polarization, line, pixel): swath_indices[v1][valid2] = iswath + 1 return swath_indices - def get_apg_vectors(self, polarization, line, pixel, min_size=3): + def get_eap_rsl_vectors(self, polarization, line, pixel, min_size=3): """ Compute Antenna Pattern Gain APG=(1/EAP/RSL)**2 for each noise vectors """ - apg_vectors = [np.zeros(p.size) + np.nan for p in pixel] + eap_vectors = [np.zeros(p.size) + np.nan for p in pixel] + rsl_vectors = [np.zeros(p.size) + np.nan for p in pixel] swath_indices = self.get_swath_id_vectors(polarization, line, pixel) - + for swid in self.swath_ids: swath_name = f'{self.obsMode}{swid}' eap_interpolator = self.get_eap_interpolator(swath_name, polarization) @@ -213,9 +214,15 @@ def get_apg_vectors(self, polarization, line, pixel, min_size=3): ba = ba_interpolator(l, p[gpi]).flatten() eap = eap_interpolator(ba).flatten() rsl = rsl_interpolator(l, p[gpi]).flatten() - apg = (1 / eap / rsl) ** 2 - apg_vectors[i][gpi] = apg - + eap_vectors[i][gpi] = eap + rsl_vectors[i][gpi] = rsl + + return eap_vectors, rsl_vectors + + def get_apg_vectors(self, polarization, line, pixel, min_size=3): + """ Compute Antenna Pattern Gain APG=(1/EAP/RSL)**2 for each noise vectors """ + eap_vectors, rsl_vectors = self.get_eap_rsl_vectors(polarization, line, pixel, min_size=min_size) + apg_vectors = [(1 / eap / rsl) ** 2 for (eap,rsl) in zip(eap_vectors, rsl_vectors)] return apg_vectors def get_apg_scales_offsets(self): @@ -488,17 +495,23 @@ def get_raw_sigma0_vectors(self, polarization, line, pixel, cal_s0, average_line raw_sigma0[i] = dn_mean[pixel[i]]**2 / cal_s0[i]**2 return raw_sigma0 - def get_raw_sigma0_vectors_from_full_size(self, polarization, line, pixel, sigma0_fs, average_lines=111): - """ Read DN_ values from input GeoTIff for a given lines, average in azimuth direction, - compute sigma0, and return sigma0 for given pixels - - """ - ws2 = np.floor(average_lines / 2) + def get_raw_sigma0_vectors_from_full_size(self, line, pixel, swath_ids, sigma0_fs, wsy=20, wsx=5): raw_sigma0 = [np.zeros(p.size)+np.nan for p in pixel] for i in range(line.shape[0]): - y0 = max(0, line[i]-ws2) - y1 = min(sigma0_fs.shape[0], line[i]+ws2) - raw_sigma0[i] = np.nanmean(sigma0_fs[int(y0):int(y1)], axis=0)[pixel[i]] + y0 = max(0, line[i]-wsy) + y1 = min(sigma0_fs.shape[0], line[i]+wsy)+1 + if wsx == 0: + raw_sigma0[i] = np.nanmean(sigma0_fs[int(y0):int(y1)], axis=0)[pixel[i]] + else: + for j in range(1,6): + gpi = swath_ids[i] == j + s0_gpi = [] + for p in pixel[i][gpi]: + x0 = max(pixel[i][gpi].min(), p-wsx) + x1 = min(pixel[i][gpi].max(), p+wsx)+1 + s0_window = sigma0_fs[int(y0):int(y1), int(x0):int(x1)] + s0_gpi.append(np.nanmean(s0_window)) + raw_sigma0[i][gpi] = s0_gpi return raw_sigma0 def get_lons_vectors_from_full_size(self, line, pixel, lons_fs): From 5af47f8995ef3c6b188f51ff5659449b243b810c Mon Sep 17 00:00:00 2001 From: akorosov Date: Wed, 4 Oct 2023 08:50:41 +0200 Subject: [PATCH 06/25] get_raw_sigma0_vectors_from_full_size : add avg_func --- s1denoise/sentinel1image.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index cdea2a6..2840aae 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -495,7 +495,7 @@ def get_raw_sigma0_vectors(self, polarization, line, pixel, cal_s0, average_line raw_sigma0[i] = dn_mean[pixel[i]]**2 / cal_s0[i]**2 return raw_sigma0 - def get_raw_sigma0_vectors_from_full_size(self, line, pixel, swath_ids, sigma0_fs, wsy=20, wsx=5): + def get_raw_sigma0_vectors_from_full_size(self, line, pixel, swath_ids, sigma0_fs, wsy=20, wsx=5, avg_func=np.nanmean): raw_sigma0 = [np.zeros(p.size)+np.nan for p in pixel] for i in range(line.shape[0]): y0 = max(0, line[i]-wsy) @@ -510,7 +510,7 @@ def get_raw_sigma0_vectors_from_full_size(self, line, pixel, swath_ids, sigma0_f x0 = max(pixel[i][gpi].min(), p-wsx) x1 = min(pixel[i][gpi].max(), p+wsx)+1 s0_window = sigma0_fs[int(y0):int(y1), int(x0):int(x1)] - s0_gpi.append(np.nanmean(s0_window)) + s0_gpi.append(avg_func(s0_window)) raw_sigma0[i][gpi] = s0_gpi return raw_sigma0 From c29a2490515bab389b353d2db9eacabc4cdda59f Mon Sep 17 00:00:00 2001 From: akorosov Date: Wed, 4 Oct 2023 08:51:49 +0200 Subject: [PATCH 07/25] add collect_apg_data.py update_apg_coefficients.py --- training/collect_apg_data.py | 117 +++++++++++++++++++++++++ training/update_apg_coefficients.py | 130 ++++++++++++++++++++++++++++ 2 files changed, 247 insertions(+) create mode 100755 training/collect_apg_data.py create mode 100755 training/update_apg_coefficients.py diff --git a/training/collect_apg_data.py b/training/collect_apg_data.py new file mode 100755 index 0000000..9ad0839 --- /dev/null +++ b/training/collect_apg_data.py @@ -0,0 +1,117 @@ +#!/usr/bin/env python + +""" This python script process individual S1 Level-1 GRD files to get values of antenna pattern gain (APG), sigma0, incidence angle for each noise vector + +run example: +python collect_apg_data.py /path/to/L1/GRD/files /path/to/output/dir + +""" +import argparse +from collections import defaultdict +import glob +import os +from pathlib import Path + +from bs4 import BeautifulSoup +import numpy as np + +from s1denoise import Sentinel1Image + +def read_kproc(s1): + soup = BeautifulSoup(s1.annotationXML['HV'].toxml()) + kprop = {spp.swath.text: float(spp.processorscalingfactor.text) + for spp in soup.find_all('swathprocparams')} + return kprop + +def get_pgpp_pgpa(s1): + soup = BeautifulSoup(s1.annotationXML['HV'].toxml()) + pgpp = soup.find_all('pgproductphase') + pgpa = soup.find_all('pgproductamplitude') + pha_dict = defaultdict(list) + amp_dict = defaultdict(list) + + for pg in pgpp: + pha_dict[pg.parent.parent.parent.swath.text].append(float(pg.text)) + for pg in pgpa: + amp_dict[pg.parent.parent.parent.swath.text].append(float(pg.text)) + return pha_dict, amp_dict + +def get_mean_pgpp_pgpa(s1): + pha_dict, amp_dict = get_pgpp_pgpa(s1) + pha_dict_avg = {i: np.mean(pha_dict[i]) for i in pha_dict} + amp_dict_avg = {i: np.mean(amp_dict[i]) for i in amp_dict} + return pha_dict_avg, amp_dict_avg + +def parse_run_experiment_args(): + """ Parse input args for run_experiment_* scripts """ + parser = argparse.ArgumentParser(description='Process SAFE or ZIP files and collect APG related values') + parser.add_argument('-o', '--out-dir', type=Path) + parser.add_argument('-f', '--force', action='store_true', help="Force overwrite existing output files") + parser.add_argument('-i', '--inp-files', type=Path, nargs='+') + + return parser.parse_args() + +def main(): + polarization = 'HV' + args = parse_run_experiment_args() + print('Number of files queued:', len(args.inp_files)) + + for ifile in args.inp_files: + ofile = os.path.join(args.out_dir, os.path.basename(ifile) + '_apg.npz') + if os.path.exists(ofile) and not args.force: + print(ofile, 'exists') + continue + + print(ifile) + s1 = Sentinel1Image(str(ifile)) + try: + dn_hv = s1.get_GDALRasterBand('DN_HV').ReadAsArray().astype(float) + except RuntimeError: + print('GDAL cannot open ', ifile) + continue + + line, pixel, noise = s1.get_noise_range_vectors(polarization) + swath_ids = s1.get_swath_id_vectors(polarization, line, pixel) + eap, rsl = s1.get_eap_rsl_vectors(polarization, line, pixel) + ea_interpolator, ia_interpolator = s1.get_elevation_incidence_angle_interpolators(polarization) + eleang, incang = s1.get_elevation_incidence_angle_vectors(ea_interpolator, ia_interpolator, line, pixel) + cal_s0hv = s1.get_calibration_vectors(polarization, line, pixel) + scall_hv = s1.get_noise_azimuth_vectors(polarization, line, pixel) + + dn_hv[dn_hv < 20] = np.nan + sigma0hv = s1.get_raw_sigma0_vectors_from_full_size(line, pixel, swath_ids, dn_hv) + sigma0hv_p10 = s1.get_raw_sigma0_vectors_from_full_size(line, pixel, swath_ids, dn_hv, avg_func=lambda x: np.nanpercentile(x, 10)) + sigma0hv_p20 = s1.get_raw_sigma0_vectors_from_full_size(line, pixel, swath_ids, dn_hv, avg_func=lambda x: np.nanpercentile(x, 20)) + + pgpp, pgpa = get_pgpp_pgpa(s1) + kproc = read_kproc(s1) + eap_xml = s1.import_elevationAntennaPattern('HV') + ncf = {i: eap_xml[i]['noiseCalibrationFactor'] for i in eap_xml} + acc = eap_xml['EW1']['absoluteCalibrationConstant'] + ipf = s1.IPFversion + + np.savez( + ofile, + line=line, + pixel=pixel, + noise=noise, + swath_ids=swath_ids, + eap=eap, + rsl=rsl, + eleang=eleang, + incang=incang, + cal_s0hv=cal_s0hv, + scall_hv=scall_hv, + sigma0hv=sigma0hv, + sigma0hv_p10=sigma0hv_p10, + sigma0hv_p20=sigma0hv_p20, + pgpp=pgpp, + pgpa=pgpa, + kproc=kproc, + ncf=ncf, + acc=acc, + ipf=ipf, + ) + +if __name__ == "__main__": + main() diff --git a/training/update_apg_coefficients.py b/training/update_apg_coefficients.py new file mode 100755 index 0000000..731d7bc --- /dev/null +++ b/training/update_apg_coefficients.py @@ -0,0 +1,130 @@ +#!/usr/bin/env python + +""" This python script collects values of antenna pattern gain (APG), sigma0, incidence angle, etc from pre-processed files. +Then it computes coefficient B for equation: +Y = X B + +Where Y is sigma0_HV, and X is a mtrix with the following columns: +[1, incidence_angel, apg_ew1, 1, apg_ew2, 1, apg_ew3, 1, apg_ew4, 1, apg_ew5, 1] +The first two columns have values for all rows, other columns have values only for correposning sub-swaths, or zeros for other subswaths. + +Coefficient B is computed for each combination of platform, polarisation, mode, IPF +The results are saved in denoising_parameters.json + +run example: +python process_apg_data.py /path/to/APG-files + +""" +import argparse +from collections import defaultdict +import glob +import json +import os +from pathlib import Path + +import numpy as np +import matplotlib.pyplot as plt + +from s1denoise.utils import skip_swath_borders, build_AY_matrix, solve +from update_parameter_file import safe_load + +array_names = ['line', + 'pixel', + 'noise', + 'swath_ids', + 'eap', + 'rsl', + 'eleang', + 'incang', + 'cal_s0hv', + 'scall_hv', + 'sigma0hv', +] + +item_names = [ + 'pgpp', + 'pgpa', + 'kproc', + 'ncf', + 'acc', + 'ipf' +] + +keep_names = ['ipf', 'sigma0hv', 'swath_ids', 'incang'] + +polarization = 'HV' +scale_APG = 1e21 +scale_HV = 1000 +s0hv_max = [None, 3.0, 1.3, 1.0, 0.9, 0.8] + +def parse_run_experiment_args(): + """ Parse input args for run_experiment_* scripts """ + parser = argparse.ArgumentParser(description='Process SAFE or ZIP files and collect APG related values') + parser.add_argument('inp_dir', type=Path) + parser.add_argument('out_file', type=Path) + return parser.parse_args() + + +args = parse_run_experiment_args() +ifiles = sorted(glob.glob(f'{args.inp_dir}/S1*_apg.npz')) + +l = defaultdict(list) +for ifile in ifiles: + print('Read', ifile) + ds = np.load(ifile, allow_pickle=True) + d = {n: ds[n] for n in array_names} + d.update({n: ds[n].item() for n in item_names}) + sigma0hv = d['sigma0hv'] ** 2 / d['cal_s0hv'] ** 2 + apg = (1 / d['eap'] / d['rsl']) ** 2 / d['cal_s0hv'] ** 2 * d['scall_hv'] + l['ipf'].append(d['ipf']) + l['apg'].append(apg) + l['sigma0hv'].append(sigma0hv) + l['swath_ids'].append(d['swath_ids']) + l['incang'].append(d['incang']) + +ll = defaultdict(list) +for ipf, apg, sigma0hv, swath_ids, incang, ifile in zip(l['ipf'], l['apg'], l['sigma0hv'], l['swath_ids'], l['incang'], ifiles): + print('Build', ifile) + name_parts = os.path.basename(ifile).split('_') + platform, mode, resolution, pol = name_parts[0], name_parts[1], name_parts[2], name_parts[3] + uid = f'{platform}_{mode}_{resolution}_{pol}_APG_{ipf:04.2f}' + swath_ids_skip = skip_swath_borders(swath_ids, skip=2) + sigma0hv_s = [i * scale_HV for i in sigma0hv] + apg_s = [i * scale_APG for i in apg] + A, Y = build_AY_matrix(swath_ids_skip, sigma0hv_s, apg_s, incang, s0hv_max) + if A is not None: + ll['a'].append(A) + ll['y'].append(Y) + ll['uid'].append(uid) + +uids, uid_inverse = np.unique(ll['uid'], return_inverse=True) +B = {} +rmsd = {} + +for i, uid in enumerate(uids): + print('Solve', uid) + uid_indices = np.where(uid_inverse == i)[0] + A = [ll['a'][uid_idx] for uid_idx in uid_indices] + Y = [ll['y'][uid_idx] for uid_idx in uid_indices] + A = np.vstack(A) + Y = np.vstack(Y) + B[uid], rmsd[uid] = solve(A, Y) + Yrec = np.dot(A, B[uid]) + plt.plot(Y.flat, Yrec, 'k.', alpha=0.1) + plt.title(uid) + plt.savefig(f'{uid}_quality.png') + plt.close() + + +p = safe_load(args.out_file) +for uid in B: + print('Save', uid) + p[uid] = dict( + Y_SCALE = scale_HV, + A_SCALE = scale_APG, + B = B[uid].tolist(), + RMSD = rmsd[uid], + ) + +with open(args.out_file, "w") as f: + json.dump(p, f) \ No newline at end of file From e44cf82591ef0eb10c778474b6bd58fb1c1ba4a3 Mon Sep 17 00:00:00 2001 From: akorosov Date: Fri, 6 Oct 2023 16:53:11 +0200 Subject: [PATCH 08/25] add get_subswathScallopingGain --- .gitignore | 1 + s1denoise/denoising_parameters.json | 2 +- s1denoise/sentinel1image.py | 385 ++++++++++++++++++++++------ training/collect_apg_data.py | 4 - training/update_apg_coefficients.py | 32 ++- 5 files changed, 332 insertions(+), 92 deletions(-) diff --git a/.gitignore b/.gitignore index 7d12672..70f67c5 100644 --- a/.gitignore +++ b/.gitignore @@ -66,3 +66,4 @@ target/ .DS_Store fit_on_one +*png diff --git a/s1denoise/denoising_parameters.json b/s1denoise/denoising_parameters.json index 09d8129..bb02716 100644 --- a/s1denoise/denoising_parameters.json +++ b/s1denoise/denoising_parameters.json @@ -1 +1 @@ -{"S1A_EW_GRDM_HV_NS_2.4": {"EW1": 1.2226739798419997, "EW2": 0.9615959470712395, "EW3": 1.0292391382243975, "EW4": 1.0065355608166793, "EW5": 0.9218665616511147}, "S1A_EW_GRDM_HV_NS_2.5": {"EW1": 1.2171388595679526, "EW2": 0.9536877350555699, "EW3": 1.015597864698578, "EW4": 0.9970741241853454, "EW5": 0.92307724873829}, "S1A_EW_GRDM_HV_NS_2.6": {"EW1": 1.1719745307602576, "EW2": 0.9163712571049173, "EW3": 0.9681532184092376, "EW4": 0.9430584692825155, "EW5": 0.8780597521669482}, "S1A_EW_GRDM_HV_NS_2.7": {"EW1": 1.398936536855681, "EW2": 0.9814810558391361, "EW3": 1.0515762397929336, "EW4": 1.0190944087059224, "EW5": 0.9595736050922239}, "S1A_EW_GRDM_HV_NS_2.8": {"EW1": 1.3043153896695185, "EW2": 0.9763390733024808, "EW3": 0.9975155379952487, "EW4": 0.9348512736439629, "EW5": 0.9625411730186109}, "S1A_EW_GRDM_HV_NS_2.9": {"EW1": 1.4807347002871454, "EW2": 1.0029777509123536, "EW3": 0.9874088104539668, "EW4": 1.057951324021166, "EW5": 1.0227437274453466}, "S1A_EW_GRDM_HV_PB_2.4": {"EW1": 7.830272905064692e-07, "EW2": 4.449220497638743e-06, "EW3": 4.718034189102612e-05, "EW4": 5.477365452456807e-06, "EW5": -3.1348551872009446e-06}, "S1A_EW_GRDM_HV_PB_2.5": {"EW1": 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0.029179334910631315, 0.561641897646114, 0.1566830975417528, 0.5816846667833477, 0.20793171903681174, 0.4277717148711025, 0.5892364653892982, -0.015285408508978526], "RMSD": 0.13900436917238226}, "S1A_EW_GRDM_1SDH_APG_2.71": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.0507397703038304, 0.9863534274981022, 0.026205634744561057, 0.6900429422291734, -0.021143123724448244, 0.5549163011189219, 0.07502340421480437, 0.5535463249832671, 0.08209676742831895, 0.41074347461225597, 0.2129224529670666, -0.003975617872727932], "RMSD": 0.10380762774462386}, "S1A_EW_GRDM_1SDH_APG_2.82": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.1654378938381603, 0.9037911861025114, 0.007571923901070532, 0.6617721702036207, -0.08519719861886524, 0.5296478665525468, -0.06257197765332109, 0.5061070256039071, -0.10935194968666409, 0.41049398884667576, -0.08411130821962128, 0.00578162871309873], "RMSD": 0.11193658002208837}, "S1A_EW_GRDM_1SDH_APG_2.91": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.09633071107374985, 0.878893837497406, 0.025088204945688863, 0.6056575050691091, -0.058052349740865455, 0.491208499507367, -0.01777702680468954, 0.4800135878954776, -0.04329607065288238, 0.3897812373359747, 0.0022934688210014473, 0.0032918690774513815], "RMSD": 0.1106507425291071}, "S1A_EW_GRDM_1SDH_APG_3.10": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.12767885740602244, 0.8800162161325317, 0.03182739490915809, 0.6010529798826316, -0.09432391104094813, 0.49716761718731206, -0.06571360589627237, 0.4750756261393592, -0.11279012229924934, 0.38811917829073256, -0.11332138692128918, 0.007275231044963082], "RMSD": 0.11124646412407072}, "S1A_EW_GRDM_1SDH_APG_3.31": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.11476513117203134, 0.8934545348379068, 0.033330808571952586, 0.6123423536143044, -0.06983030372492582, 0.5020156802815021, -0.04153026963015755, 0.4852562838792013, -0.06545415206146064, 0.3924143825000602, -0.02871878567255865, 0.004295774585718948], "RMSD": 0.11263706959172753}, "S1A_EW_GRDM_1SDH_APG_3.52": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.07175980815432235, 0.9185203846645115, 0.0287394169413826, 0.6174067707175318, -0.0554848691429293, 0.5016305611704198, -0.05381280100189009, 0.4920683222950097, -0.08062911198893755, 0.3998657550459244, -0.08942755703805166, 0.0055893198138603495], "RMSD": 0.09886363440571459}, "S1A_EW_GRDM_1SDH_APG_3.61": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.08632782693067194, 0.9151004958112149, 0.026023795199221378, 0.6125411438398006, -0.09628692971085874, 0.50265667025664, -0.0589679525744893, 0.4824799345074508, -0.11511677480876054, 0.39774282978353304, -0.15802003496421416, 0.00841830982872438], "RMSD": 0.1094321464944876}, "S1B_EW_GRDM_1SDH_APG_2.82": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.25126102362177133, 0.7694751481707963, 0.05183005557132159, 0.47239789543396443, -0.14655408171810194, 0.41534793683113963, -0.1675317146319409, 0.385329272819766, -0.2397855300150744, 0.29586848225018825, 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0.0266540498761616, 0.6977094871346537, 0.03903248976520473, 0.541043209281432, 0.0707796191811445, 0.600809657836981, 0.10895777383597607, 0.4218554342385637, 0.45474578323453924, -0.011141119529497923], "RMSD": 0.1191453310575453}, "S1A_EW_GRDM_1SDH_APG_2.84": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.1225774572606287, 0.892604551689341, 0.05202822874017787, 0.640070720515214, -0.026776157668149107, 0.5285113533737578, 0.03689360838511655, 0.5394856595600113, 0.04278113726108867, 0.43426894529303733, 0.22750427397886108, -0.004133082544622857], "RMSD": 0.11414860716236912}, "S1B_EW_GRDM_1SDH_APG_2.84": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.20377648097409218, 0.784736346725933, 0.10451052744897327, 0.46736091859066164, -0.07084770648554543, 0.4121792309942518, -0.06577055072564891, 0.39726993398663196, -0.15227493893437066, 0.3367914408553189, 0.01939381227750215, 0.005091163334962512], "RMSD": 0.19276230065711977}} \ No newline at end of file diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index 2840aae..abd0be9 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -1,17 +1,19 @@ +from collections import defaultdict from datetime import datetime, timedelta import glob import json import os import requests import shutil -import subprocess import xml.etree.ElementTree as ET from xml.dom.minidom import parse, parseString import zipfile +from functools import cached_property from nansat import Nansat import numpy as np from osgeo import gdal +from bs4 import BeautifulSoup from scipy.interpolate import InterpolatedUnivariateSpline, RectBivariateSpline from scipy.optimize import minimize from scipy.interpolate import interp1d @@ -26,6 +28,16 @@ ) SPEED_OF_LIGHT = 299792458. +RADAR_FREQUENCY = 5.405000454334350e+09 +RADAR_WAVELENGTH = SPEED_OF_LIGHT / RADAR_FREQUENCY +ANTENNA_STEERING_RATE = { 'IW1': 1.590368784, + 'IW2': 0.979863325, + 'IW3': 1.397440818, + 'EW1': 2.390895448, + 'EW2': 2.811502724, + 'EW3': 2.366195855, + 'EW4': 2.512694636, + 'EW5': 2.122855427 } # degrees per second. Available from AUX_INS class Sentinel1Image(Nansat): @@ -118,6 +130,14 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): self.download_aux_calibration(auxCalibFilename, self.platform.lower()) self.auxiliaryCalibrationXML = parse(self.auxiliaryCalibration_file) + @cached_property + def scallopingGain(self, polarization='HV'): + sg = {} + for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): + subswathID = '%s%s' % (self.obsMode, iSW) + sg[subswathID] = self.get_subswathScallopingGain(polarization, subswathID) + return sg + def set_aux_data_dir(self): """ Set directory where aux calibration data is stored """ self.aux_data_dir = os.path.join(os.environ.get('XDG_DATA_HOME', os.path.expanduser('~')), @@ -169,7 +189,6 @@ def get_noise_range_vectors(self, polarization): for pix, n in zip(noiseRangeVector['pixel'], noiseRangeVector['noiseRangeLut']): n = np.array(n) - n[n == 0] = np.nan noise.append(n) pixel.append(np.array(pix)) @@ -261,9 +280,12 @@ def get_swath_interpolator(self, polarization, swath_name, line, pixel, z): (pixel[v1] >= frs) * (pixel[v1] <= lrs) * np.isfinite(z[v1]))[0] - # interpolator for one line - z_interp1 = InterpolatedUnivariateSpline(pixel[v1][valid2], z[v1][valid2]) - z_vecs.append(z_interp1(pix_vec_fr)) + if valid2.size == 0: + z_vecs.append(np.zeros(pix_vec_fr.shape) + np.nan) + else: + # interpolator for one line + z_interp1 = InterpolatedUnivariateSpline(pixel[v1][valid2], z[v1][valid2]) + z_vecs.append(z_interp1(pix_vec_fr)) # interpolator for one subswath z_interp2 = RectBivariateSpline(swath_lines, pix_vec_fr, np.array(z_vecs)) return z_interp2, swath_coords @@ -306,31 +328,6 @@ def get_calibration_vectors(self, polarization, line, pixel, name='sigmaNought') sigma0_vecs[v1][valid2] = sigma0interp(line[v1], pixel[v1][valid2]) return sigma0_vecs - def get_noise_azimuth_vectors(self, polarization, line, pixel): - """ Interpolate scalloping noise from XML files to input pixel/lines coords """ - scall = [np.zeros(p.size)+np.nan for p in pixel] - noiseRangeVector, noiseAzimuthVector = self.import_noiseVector(polarization) - for iSW in self.swath_ids: - subswathID = '%s%s' % (self.obsMode, iSW) - numberOfBlocks = len(noiseAzimuthVector[subswathID]['firstAzimuthLine']) - for iBlk in range(numberOfBlocks): - frs = noiseAzimuthVector[subswathID]['firstRangeSample'][iBlk] - lrs = noiseAzimuthVector[subswathID]['lastRangeSample'][iBlk] - fal = noiseAzimuthVector[subswathID]['firstAzimuthLine'][iBlk] - lal = noiseAzimuthVector[subswathID]['lastAzimuthLine'][iBlk] - y = np.array(noiseAzimuthVector[subswathID]['line'][iBlk]) - z = np.array(noiseAzimuthVector[subswathID]['noiseAzimuthLut'][iBlk]) - if y.size > 1: - nav_interp = InterpolatedUnivariateSpline(y, z, k=1) - else: - nav_interp = lambda x: z - - line_gpi = np.where((line >= fal) * (line <= lal))[0] - for line_i in line_gpi: - pixel_gpi = np.where((pixel[line_i] >= frs) * (pixel[line_i] <= lrs))[0] - scall[line_i][pixel_gpi] = nav_interp(line[line_i]) - return scall - def calibrate_noise_vectors(self, noise, cal_s0, scall): """ Compute calibrated NESZ from input noise, sigma0 calibration and scalloping noise""" return [s * n / c**2 for n, c, s in zip(noise, cal_s0, scall)] @@ -404,12 +401,12 @@ def get_range_spread_loss_interpolator(self, polarization): rsp_interpolator = RectBivariateSpline(yggp, xggp, rangeSpreadingLoss) return rsp_interpolator - def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip = 4): + def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip=4, min_valid_size=10): """ Estimate shift in range noise LUT relative to antenna gain pattern and correct for it. """ - noise_shifted = [np.zeros(p.size)+np.nan for p in pixel] + noise_shifted = [np.zeros(p.size) for p in pixel] swathBounds = self.import_swathBounds(polarization) # noise lut shift for swid in self.swath_ids: @@ -433,23 +430,24 @@ def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip = 4): (pixel[v1] >= frs) * (pixel[v1] <= lrs) * (np.isfinite(noise[v1])))[0] - # keep only unique pixels - valid_pix, valid_pix_i = np.unique(pixel[v1][valid2], return_index=True) - valid2 = valid2[valid_pix_i] - - ba = ba_interpolator(valid_lin, valid_pix).flatten() - eap = eap_interpolator(ba).flatten() - rsp = rsp_interpolator(valid_lin, valid_pix).flatten() - apg = (1/eap/rsp)**2 - - noise_valid = np.array(noise[v1][valid2]) - if np.allclose(noise_valid, noise_valid[0]): - noise_shifted[v1][valid2] = noise_valid - else: - noise_interpolator = InterpolatedUnivariateSpline(valid_pix, noise_valid) - pixel_shift = minimize(cost, 0, args=(valid_pix[skip:-skip], noise_interpolator, apg[skip:-skip])).x[0] - noise_shifted0 = noise_interpolator(valid_pix + pixel_shift) - noise_shifted[v1][valid2] = noise_shifted0 + if valid2.size >= min_valid_size: + # keep only unique pixels + valid_pix, valid_pix_i = np.unique(pixel[v1][valid2], return_index=True) + valid2 = valid2[valid_pix_i] + + ba = ba_interpolator(valid_lin, valid_pix).flatten() + eap = eap_interpolator(ba).flatten() + rsp = rsp_interpolator(valid_lin, valid_pix).flatten() + apg = (1/eap/rsp)**2 + + noise_valid = np.array(noise[v1][valid2]) + if np.allclose(noise_valid, noise_valid[0]): + noise_shifted[v1][valid2] = noise_valid + else: + noise_interpolator = InterpolatedUnivariateSpline(valid_pix, noise_valid) + pixel_shift = minimize(cost, 0, args=(valid_pix[skip:-skip], noise_interpolator, apg[skip:-skip])).x[0] + noise_shifted0 = noise_interpolator(valid_pix + pixel_shift) + noise_shifted[v1][valid2] = noise_shifted0 return noise_shifted @@ -775,32 +773,6 @@ def experiment_powerBalancing(self, polarization, average_lines=777, zoom_step=2 np.savez(self.filename.split('.')[0] + '_powerBalancing.npz', **results) - def get_scalloping_full_size(self, polarization): - """ Interpolate noise azimuth vector to full resolution for all blocks """ - scall_fs = np.zeros(self.shape()) + np.nan - noiseRangeVector, noiseAzimuthVector = self.import_noiseVector(polarization) - swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] - for swath_name in swath_names: - nav = noiseAzimuthVector[swath_name] - zipped = zip( - nav['firstAzimuthLine'], - nav['lastAzimuthLine'], - nav['firstRangeSample'], - nav['lastRangeSample'], - nav['line'], - nav['noiseAzimuthLut'], - ) - for fal, lal, frs, lrs, y, z in zipped: - if isinstance(y, list): - nav_interp = InterpolatedUnivariateSpline(y, z, k=1) - else: - nav_interp = lambda x: z - lin_vec_fr = np.arange(fal, lal+1) - z_vec_fr = nav_interp(lin_vec_fr) - z_arr = np.repeat([z_vec_fr], (lrs-frs+1), axis=0).T - scall_fs[fal:lal+1, frs:lrs+1] = z_arr - return scall_fs - def interp_nrv_full_size(self, z, line, pixel, polarization, power=1): """ Interpolate noise range vectors to full size """ z_fs = np.zeros(self.shape()) + np.nan @@ -1261,3 +1233,266 @@ def remove_texture_noise(self, polarization, window=3, weight=0.15, s0_min=0, re sigma0o = fill_gaps(sigma0o, sigma0o <= s0_min) return sigma0o + + def subswathIndexMap(self, polarization): + ''' Convert subswath indices into full grid pixels ''' + subswathIndexMap = np.zeros(self.shape(), dtype=np.uint8) + swathBounds = self.import_swathBounds(polarization) + for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): + swathBound = swathBounds['%s%s' % (self.obsMode, iSW)] + zipped = zip(swathBound['firstAzimuthLine'], + swathBound['firstRangeSample'], + swathBound['lastAzimuthLine'], + swathBound['lastRangeSample']) + for fal, frs, lal, lrs in zipped: + subswathIndexMap[fal:lal+1,frs:lrs+1] = iSW + return subswathIndexMap + + def import_azimuthAntennaElementPattern(self, polarization): + ''' Import azimuth antenna element pattern from auxiliary calibration XML DOM ''' + calParamsList = self.auxiliaryCalibrationXML.getElementsByTagName('calibrationParams') + azimuthAntennaElementPattern = { '%s%s' % (self.obsMode, li): + { 'azimuthAngleIncrement':[], + 'azimuthAntennaElementPattern':[], + 'absoluteCalibrationConstant':[], + 'noiseCalibrationFactor':[] } + for li in range(1, {'IW':3, 'EW':5}[self.obsMode]+1) } + for iList in calParamsList: + swath = get_DOM_nodeValue(iList,['swath']) + pol = get_DOM_nodeValue(iList,['polarisation']) + if (swath in azimuthAntennaElementPattern.keys()) and (pol==polarization): + elem = iList.getElementsByTagName('azimuthAntennaElementPattern')[0] + for k in azimuthAntennaElementPattern[swath].keys(): + if k=='azimuthAngleIncrement': + azimuthAntennaElementPattern[swath][k] = ( + get_DOM_nodeValue(elem,[k],'float') ) + elif k=='azimuthAntennaElementPattern': + azimuthAntennaElementPattern[swath][k] = ( + get_DOM_nodeValue(elem,['values'],'float') ) + else: + azimuthAntennaElementPattern[swath][k] = ( + get_DOM_nodeValue(iList,[k],'float') ) + return azimuthAntennaElementPattern + + def subswathCenterSampleIndex(self, polarization): + ''' Range center pixel indices along azimuth for each subswath ''' + swathBounds = self.import_swathBounds(polarization) + subswathCenterSampleIndex = {} + for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): + subswathID = '%s%s' % (self.obsMode, iSW) + numberOfLines = ( np.array(swathBounds[subswathID]['lastAzimuthLine']) + - np.array(swathBounds[subswathID]['firstAzimuthLine']) + 1 ) + midPixelIndices = ( np.array(swathBounds[subswathID]['firstRangeSample']) + + np.array(swathBounds[subswathID]['lastRangeSample']) ) / 2. + subswathCenterSampleIndex[subswathID] = int(round( + np.sum(midPixelIndices * numberOfLines) / np.sum(numberOfLines) )) + return subswathCenterSampleIndex + + def geolocationGridPointInterpolator(self, polarization, itemName): + ''' Generate interpolator for items in geolocation grid point list ''' + geolocationGridPoint = self.import_geolocationGridPoint(polarization) + if itemName not in geolocationGridPoint.keys(): + raise ValueError('%s is not in the geolocationGridPoint list.' % itemName) + x = np.unique(geolocationGridPoint['pixel']) + y = np.unique(geolocationGridPoint['line']) + if itemName=='azimuthTime': + z = [ (t-self.time_coverage_center).total_seconds() + for t in geolocationGridPoint['azimuthTime'] ] + z = np.reshape(z,(len(y),len(x))) + else: + z = np.reshape(geolocationGridPoint[itemName],(len(y),len(x))) + interpolator = RectBivariateSpline(y, x, z) + return interpolator + + def azimuthFmRateAtGivenTime(self, polarization, relativeAzimuthTime, slantRangeTime): + ''' Get azimuth frequency modulation rate for given time vectors + + Returns + ------- + vector for all pixels in azimuth direction + ''' + if relativeAzimuthTime.size != slantRangeTime.size: + raise ValueError('relativeAzimuthTime and slantRangeTime must have the same dimension') + azimuthFmRate = self.import_azimuthFmRate(polarization) + azimuthFmRatePolynomial = np.array(azimuthFmRate['azimuthFmRatePolynomial']) + t0 = np.array(azimuthFmRate['t0']) + xp = np.array([ (t-self.time_coverage_center).total_seconds() + for t in azimuthFmRate['azimuthTime'] ]) + azimuthFmRateAtGivenTime = [] + for tt in zip(relativeAzimuthTime,slantRangeTime): + fp = ( azimuthFmRatePolynomial[:,0] + + azimuthFmRatePolynomial[:,1] * (tt[1]-t0)**1 + + azimuthFmRatePolynomial[:,2] * (tt[1]-t0)**2 ) + azimuthFmRateAtGivenTime.append(np.interp(tt[0], xp, fp)) + return np.squeeze(azimuthFmRateAtGivenTime) + + def import_azimuthFmRate(self, polarization): + ''' Import azimuth frequency modulation rate from annotation XML DOM ''' + soup = BeautifulSoup(self.annotationXML[polarization].toxml(), "lxml") + azimuthFmRate = defaultdict(list) + for afmr in soup.find_all('azimuthfmrate'): + azimuthFmRate['azimuthTime'].append(datetime.strptime(afmr.azimuthtime.text, '%Y-%m-%dT%H:%M:%S.%f')) + azimuthFmRate['t0'].append(float(afmr.t0.text)) + if 'azimuthfmratepolynomial' in afmr.decode(): + afmrp = list(map(float,afmr.azimuthfmratepolynomial.text.split(' '))) + elif 'c0' in afmr.decode() and 'c1' in afmr.decode() and 'c2' in afmr.decode(): + afmrp = [float(afmr.c0.text), float(afmr.c1.text), float(afmr.c2.text)] + azimuthFmRate['azimuthFmRatePolynomial'].append(afmrp) + return azimuthFmRate + + def focusedBurstLengthInTime(self, polarization): + ''' Get focused burst length in zero-Doppler time domain + + Returns + ------- + focusedBurstLengthInTime : dict + one values for each subswath (different for IW and EW) + ''' + azimuthFrequency = get_DOM_nodeValue( + self.annotationXML[polarization],['azimuthFrequency'],'float') + azimuthTimeIntevalInSLC = 1. / azimuthFrequency + inputDimensionsList = self.annotationXML[polarization].getElementsByTagName( + 'inputDimensions') + focusedBurstLengthInTime = {} + # nominalLinesPerBurst should be smaller than the real values + nominalLinesPerBurst = {'IW':1450, 'EW':1100}[self.obsMode] + for iList in inputDimensionsList: + swath = get_DOM_nodeValue(iList,['swath'],'str') + numberOfInputLines = get_DOM_nodeValue(iList,['numberOfInputLines'],'int') + numberOfBursts = max( + [ primeNumber for primeNumber in range(1,numberOfInputLines//nominalLinesPerBurst+1) + if (numberOfInputLines % primeNumber)==0 ] ) + if (numberOfInputLines % numberOfBursts)==0: + focusedBurstLengthInTime[swath] = ( + numberOfInputLines / numberOfBursts * azimuthTimeIntevalInSLC ) + else: + raise ValueError('number of bursts cannot be determined.') + return focusedBurstLengthInTime + + def get_subswathScallopingGain(self, polarization, subswathID): + # azimuth antenna element patterns (AAEP) lookup table for given subswath + AAEP = self.import_azimuthAntennaElementPattern(polarization)[subswathID] + gainAAEP = np.array(AAEP['azimuthAntennaElementPattern']) + angleAAEP = ( np.arange(-(len(gainAAEP)//2), len(gainAAEP)//2+1) + * AAEP['azimuthAngleIncrement'] ) + # subswath range center pixel index + subswathCenterSampleIndex = self.subswathCenterSampleIndex(polarization)[subswathID] + # slant range time along subswath range center + interpolator = self.geolocationGridPointInterpolator(polarization, 'slantRangeTime') + slantRangeTime = np.squeeze(interpolator(np.arange(self.shape()[0]), subswathCenterSampleIndex)) + # relative azimuth time along subswath range center + interpolator = self.geolocationGridPointInterpolator(polarization, 'azimuthTime') + azimuthTime = np.squeeze(interpolator(np.arange(self.shape()[0]), subswathCenterSampleIndex)) + # Doppler rate induced by satellite motion + motionDopplerRate = self.azimuthFmRateAtGivenTime(polarization, azimuthTime, slantRangeTime) + # antenna steering rate + antennaSteeringRate = np.deg2rad(ANTENNA_STEERING_RATE[subswathID]) + # satellite absolute velocity along subswath range center + satelliteVelocity = np.linalg.norm(self.orbitAtGivenTime(polarization, azimuthTime)['velocityXYZ'], axis=1) + # Doppler rate induced by TOPS steering of antenna + steeringDopplerRate = 2 * satelliteVelocity / RADAR_WAVELENGTH * antennaSteeringRate + # combined Doppler rate (net effect) + combinedDopplerRate = motionDopplerRate * steeringDopplerRate / (motionDopplerRate - steeringDopplerRate) + # full burst length in zero-Doppler time + fullBurstLength = self.focusedBurstLengthInTime(polarization)[subswathID] + # zero-Doppler azimuth time at each burst start + burstStartTime = np.array([ + (t-self.time_coverage_center).total_seconds() + for t in self.import_antennaPattern(polarization)[subswathID]['azimuthTime'] ]) + # burst overlapping length + burstOverlap = fullBurstLength - np.diff(burstStartTime) + burstOverlap = np.hstack([burstOverlap[0], burstOverlap]) + # time correction + burstStartTime += burstOverlap / 2. + # if burst start time does not cover the full image, + # add more sample points using the closest burst length + while burstStartTime[0] > azimuthTime[0]: + burstStartTime = np.hstack( + [burstStartTime[0] - np.diff(burstStartTime)[0], burstStartTime]) + while burstStartTime[-1] < azimuthTime[-1]: + burstStartTime = np.hstack( + [burstStartTime, burstStartTime[-1] + np.diff(burstStartTime)[-1]]) + # convert azimuth time to burst time + burstTime = np.copy(azimuthTime) + for li in range(len(burstStartTime)-1): + valid = ( (azimuthTime >= burstStartTime[li]) + * (azimuthTime < burstStartTime[li+1]) ) + burstTime[valid] -= (burstStartTime[li] + burstStartTime[li+1]) / 2. + # compute antenna steering angle for each burst time + antennaSteeringAngle = np.rad2deg( + RADAR_WAVELENGTH / (2 * satelliteVelocity) + * combinedDopplerRate * burstTime ) + # compute scalloping gain for each burst time + burstAAEP = np.interp(antennaSteeringAngle, angleAAEP, gainAAEP) + scallopingGain = 1. / 10**(burstAAEP/10.) + return scallopingGain + + def get_noise_azimuth_vectors(self, polarization, line, pixel): + """ Interpolate scalloping noise from XML files to input line/pixel coords """ + scall = [np.zeros(p.size) for p in pixel] + if self.IPFversion < 2.9: + swath_idvs = self.get_swath_id_vectors(polarization, line, pixel) + for i, l in enumerate(line): + for j in range(1,6): + gpi = swath_idvs[i] == j + scall[i][gpi] = self.scallopingGain[f'EW{j}'][l] + return scall + + noiseRangeVector, noiseAzimuthVector = self.import_noiseVector(polarization) + for iSW in self.swath_ids: + subswathID = '%s%s' % (self.obsMode, iSW) + numberOfBlocks = len(noiseAzimuthVector[subswathID]['firstAzimuthLine']) + for iBlk in range(numberOfBlocks): + frs = noiseAzimuthVector[subswathID]['firstRangeSample'][iBlk] + lrs = noiseAzimuthVector[subswathID]['lastRangeSample'][iBlk] + fal = noiseAzimuthVector[subswathID]['firstAzimuthLine'][iBlk] + lal = noiseAzimuthVector[subswathID]['lastAzimuthLine'][iBlk] + y = np.array(noiseAzimuthVector[subswathID]['line'][iBlk]) + z = np.array(noiseAzimuthVector[subswathID]['noiseAzimuthLut'][iBlk]) + if y.size > 1: + nav_interp = InterpolatedUnivariateSpline(y, z, k=1) + else: + nav_interp = lambda x: z + + line_gpi = np.where((line >= fal) * (line <= lal))[0] + for line_i in line_gpi: + pixel_gpi = np.where((pixel[line_i] >= frs) * (pixel[line_i] <= lrs))[0] + scall[line_i][pixel_gpi] = nav_interp(line[line_i]) + return scall + + def get_scalloping_full_size(self, polarization): + """ Interpolate noise azimuth vector to full resolution for all blocks """ + scall_fs = np.zeros(self.shape()) + if self.IPFversion < 2.9: + subswathIndexMap = self.subswathIndexMap(polarization) + for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): + subswathID = '%s%s' % (self.obsMode, iSW) + scallopingGain = self.scallopingGain[subswathID] + # assign computed scalloping gain into each subswath + valid = (subswathIndexMap==iSW) + scall_fs[valid] = ( + scallopingGain[:,np.newaxis] * np.ones((1,self.shape()[1])))[valid] + return scall_fs + + noiseRangeVector, noiseAzimuthVector = self.import_noiseVector(polarization) + swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] + for swath_name in swath_names: + nav = noiseAzimuthVector[swath_name] + zipped = zip( + nav['firstAzimuthLine'], + nav['lastAzimuthLine'], + nav['firstRangeSample'], + nav['lastRangeSample'], + nav['line'], + nav['noiseAzimuthLut'], + ) + for fal, lal, frs, lrs, y, z in zipped: + if isinstance(y, list): + nav_interp = InterpolatedUnivariateSpline(y, z, k=1) + else: + nav_interp = lambda x: z + lin_vec_fr = np.arange(fal, lal+1) + z_vec_fr = nav_interp(lin_vec_fr) + z_arr = np.repeat([z_vec_fr], (lrs-frs+1), axis=0).T + scall_fs[fal:lal+1, frs:lrs+1] = z_arr + return scall_fs \ No newline at end of file diff --git a/training/collect_apg_data.py b/training/collect_apg_data.py index 9ad0839..91bc5b5 100755 --- a/training/collect_apg_data.py +++ b/training/collect_apg_data.py @@ -80,8 +80,6 @@ def main(): dn_hv[dn_hv < 20] = np.nan sigma0hv = s1.get_raw_sigma0_vectors_from_full_size(line, pixel, swath_ids, dn_hv) - sigma0hv_p10 = s1.get_raw_sigma0_vectors_from_full_size(line, pixel, swath_ids, dn_hv, avg_func=lambda x: np.nanpercentile(x, 10)) - sigma0hv_p20 = s1.get_raw_sigma0_vectors_from_full_size(line, pixel, swath_ids, dn_hv, avg_func=lambda x: np.nanpercentile(x, 20)) pgpp, pgpa = get_pgpp_pgpa(s1) kproc = read_kproc(s1) @@ -103,8 +101,6 @@ def main(): cal_s0hv=cal_s0hv, scall_hv=scall_hv, sigma0hv=sigma0hv, - sigma0hv_p10=sigma0hv_p10, - sigma0hv_p20=sigma0hv_p20, pgpp=pgpp, pgpa=pgpa, kproc=kproc, diff --git a/training/update_apg_coefficients.py b/training/update_apg_coefficients.py index 731d7bc..340b5b3 100755 --- a/training/update_apg_coefficients.py +++ b/training/update_apg_coefficients.py @@ -50,13 +50,12 @@ 'ipf' ] -keep_names = ['ipf', 'sigma0hv', 'swath_ids', 'incang'] - polarization = 'HV' scale_APG = 1e21 scale_HV = 1000 s0hv_max = [None, 3.0, 1.3, 1.0, 0.9, 0.8] + def parse_run_experiment_args(): """ Parse input args for run_experiment_* scripts """ parser = argparse.ArgumentParser(description='Process SAFE or ZIP files and collect APG related values') @@ -71,16 +70,22 @@ def parse_run_experiment_args(): l = defaultdict(list) for ifile in ifiles: print('Read', ifile) - ds = np.load(ifile, allow_pickle=True) - d = {n: ds[n] for n in array_names} + try: + ds = np.load(ifile, allow_pickle=True) + d = {n: ds[n] for n in array_names} + except: + # TEMPORARY! + # skip if file is written at the moment + continue + d.update({n: ds[n].item() for n in item_names}) sigma0hv = d['sigma0hv'] ** 2 / d['cal_s0hv'] ** 2 apg = (1 / d['eap'] / d['rsl']) ** 2 / d['cal_s0hv'] ** 2 * d['scall_hv'] l['ipf'].append(d['ipf']) - l['apg'].append(apg) - l['sigma0hv'].append(sigma0hv) - l['swath_ids'].append(d['swath_ids']) - l['incang'].append(d['incang']) + l['apg'].append(apg[1:-1]) + l['sigma0hv'].append(sigma0hv[1:-1]) + l['swath_ids'].append(d['swath_ids'][1:-1]) + l['incang'].append(d['incang'][1:-1]) ll = defaultdict(list) for ipf, apg, sigma0hv, swath_ids, incang, ifile in zip(l['ipf'], l['apg'], l['sigma0hv'], l['swath_ids'], l['incang'], ifiles): @@ -111,11 +116,14 @@ def parse_run_experiment_args(): B[uid], rmsd[uid] = solve(A, Y) Yrec = np.dot(A, B[uid]) plt.plot(Y.flat, Yrec, 'k.', alpha=0.1) - plt.title(uid) - plt.savefig(f'{uid}_quality.png') + plt.plot(Y.flat, Y.flat, 'r-') + plt.title(f'{uid} {rmsd[uid]:1.2f}') + plt.xlim([0, 6]) + plt.ylim([0, 6]) + plt.gca().set_aspect('equal') + plt.savefig(f'{uid}_{hv_name}_quality.png') plt.close() - p = safe_load(args.out_file) for uid in B: print('Save', uid) @@ -127,4 +135,4 @@ def parse_run_experiment_args(): ) with open(args.out_file, "w") as f: - json.dump(p, f) \ No newline at end of file + json.dump(p, f) From dd84721442fa638b9f6ed62e77279e08c31a5fed Mon Sep 17 00:00:00 2001 From: akorosov Date: Mon, 9 Oct 2023 09:51:01 +0200 Subject: [PATCH 09/25] add s0hv_apg_corr_min, uid_mapping --- s1denoise/denoising_parameters.json | 2 +- s1denoise/utils.py | 9 +++-- training/collect_apg_data.py | 5 ++- training/update_apg_coefficients.py | 54 +++++++++++++++++++++++++---- 4 files changed, 56 insertions(+), 14 deletions(-) diff --git a/s1denoise/denoising_parameters.json b/s1denoise/denoising_parameters.json index bb02716..bbe7f79 100644 --- a/s1denoise/denoising_parameters.json +++ b/s1denoise/denoising_parameters.json @@ -1 +1 @@ -{"S1A_EW_GRDM_HV_NS_2.4": {"EW1": 1.2226739798419997, "EW2": 0.9615959470712395, "EW3": 1.0292391382243975, "EW4": 1.0065355608166793, "EW5": 0.9218665616511147}, "S1A_EW_GRDM_HV_NS_2.5": {"EW1": 1.2171388595679526, "EW2": 0.9536877350555699, "EW3": 1.015597864698578, "EW4": 0.9970741241853454, "EW5": 0.92307724873829}, "S1A_EW_GRDM_HV_NS_2.6": {"EW1": 1.1719745307602576, "EW2": 0.9163712571049173, "EW3": 0.9681532184092376, "EW4": 0.9430584692825155, "EW5": 0.8780597521669482}, "S1A_EW_GRDM_HV_NS_2.7": {"EW1": 1.398936536855681, "EW2": 0.9814810558391361, "EW3": 1.0515762397929336, "EW4": 1.0190944087059224, "EW5": 0.9595736050922239}, "S1A_EW_GRDM_HV_NS_2.8": {"EW1": 1.3043153896695185, "EW2": 0.9763390733024808, "EW3": 0.9975155379952487, "EW4": 0.9348512736439629, "EW5": 0.9625411730186109}, "S1A_EW_GRDM_HV_NS_2.9": {"EW1": 1.4807347002871454, "EW2": 1.0029777509123536, "EW3": 0.9874088104539668, "EW4": 1.057951324021166, "EW5": 1.0227437274453466}, "S1A_EW_GRDM_HV_PB_2.4": {"EW1": 7.830272905064692e-07, "EW2": 4.449220497638743e-06, "EW3": 4.718034189102612e-05, "EW4": 5.477365452456807e-06, "EW5": -3.1348551872009446e-06}, "S1A_EW_GRDM_HV_PB_2.5": {"EW1": 4.3866672637582335e-05, "EW2": 1.0796195201333851e-05, "EW3": 4.9065318157630286e-05, "EW4": 1.3722300950028815e-05, "EW5": -2.2557295998616656e-06}, "S1A_EW_GRDM_HV_PB_2.6": {"EW1": -3.819198068783059e-05, "EW2": 1.2193826271037744e-05, "EW3": 6.131842637362156e-05, "EW4": 7.392873409112185e-05, "EW5": 6.972248890083653e-05}, "S1A_EW_GRDM_HV_PB_2.7": {"EW1": -3.5621969376463706e-05, "EW2": -3.28432147836913e-05, "EW3": 9.087565093756112e-06, "EW4": 4.712064397346431e-06, "EW5": -6.272081842249038e-07}, "S1A_EW_GRDM_HV_PB_2.8": {"EW1": 0.00010123392382994249, "EW2": 1.4530929340856857e-05, "EW3": 3.552500456185288e-05, "EW4": 1.35067592097569e-05, "EW5": 2.6700987639279118e-05}, "S1A_EW_GRDM_HV_PB_2.9": {"EW1": 8.975356126433124e-05, "EW2": -3.2072923462756304e-05, "EW3": -4.2308784256238215e-06, "EW4": -9.741424762886609e-06, "EW5": 1.4381981407809117e-05}, "S1A_EW_GRDM_HV_ES_2.9": {"EW1": [216.22073753235657, 215.99164944375804, 215.7549889444315, 215.51057780219685, 215.25823708861273, 214.99778735394585, 214.72904880891673, 214.4518415130601, 214.1659855695176, 213.871301326056, 213.56760958207863, 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0.1074679737523439}, "S1B_EW_GRDM_1SDH_APG_2.90": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.23438636972307722, 0.6963614322737117, 0.025569839947089834, 0.43798868488944287, -0.06666897666872418, 0.36672340227948347, -0.09612002507198568, 0.3492523764538781, -0.1373889212537141, 0.281766183418943, -0.04022171332425388, 0.0065075697952158285], "RMSD": 0.1526504614447126}, "S1B_EW_GRDM_1SDH_APG_3.30": {"Y_SCALE": 1000, "A_SCALE": 1e+21, "B": [0.23293616176793266, 0.7076931982165353, 0.021333565137318766, 0.44394251825365527, -0.06976526551400246, 0.3711355701314257, -0.0915405830901009, 0.3572892029265106, -0.11370381717855059, 0.2782521939597645, -0.020739938877402626, 0.005887320040442123], "RMSD": 0.15933874450264138}} \ No newline at end of file diff --git a/s1denoise/utils.py b/s1denoise/utils.py index c7d728c..faf6910 100644 --- a/s1denoise/utils.py +++ b/s1denoise/utils.py @@ -98,7 +98,7 @@ def skip_swath_borders(swath_ids, skip=1): swath_ids_skip.append(swid_skip) return swath_ids_skip -def build_AY_matrix(swath_ids, sigma0hv, apg, incang, s0hv_max): +def build_AY_matrix(swath_ids, sigma0hv, apg, incang, s0hv_max, s0hv_apg_corr_min): "HV = 1, IA, 1, APG1, 1, APG2, 1, APG3, 1, APG4, 1, APG5" A_123 = [] # [1] @@ -107,9 +107,12 @@ def build_AY_matrix(swath_ids, sigma0hv, apg, incang, s0hv_max): for iswath in range(1,6): for swath_ids_v, sigma0hv_v, apg_v, incang_v in zip(swath_ids, sigma0hv, apg, incang): - gpi = np.where(swath_ids_v == iswath)[0] + gpi = np.where((swath_ids_v == iswath) * (np.isfinite(sigma0hv_v*apg_v)))[0] # append only small enough values of HV - if np.nanmean(sigma0hv_v[gpi]) < s0hv_max[iswath]: + if ( + (np.nanmean(sigma0hv_v[gpi]) < s0hv_max[iswath]) and + (pearsonr(sigma0hv_v[gpi], apg_v[gpi])[0] > s0hv_apg_corr_min[iswath]) + ): A_123.append([ np.ones(gpi.size), incang_v[gpi], diff --git a/training/collect_apg_data.py b/training/collect_apg_data.py index 91bc5b5..e0405cf 100755 --- a/training/collect_apg_data.py +++ b/training/collect_apg_data.py @@ -8,7 +8,6 @@ """ import argparse from collections import defaultdict -import glob import os from pathlib import Path @@ -62,13 +61,13 @@ def main(): print(ofile, 'exists') continue - print(ifile) - s1 = Sentinel1Image(str(ifile)) try: + s1 = Sentinel1Image(str(ifile)) dn_hv = s1.get_GDALRasterBand('DN_HV').ReadAsArray().astype(float) except RuntimeError: print('GDAL cannot open ', ifile) continue + print('Process ', ifile) line, pixel, noise = s1.get_noise_range_vectors(polarization) swath_ids = s1.get_swath_id_vectors(polarization, line, pixel) diff --git a/training/update_apg_coefficients.py b/training/update_apg_coefficients.py index 340b5b3..2460cd0 100755 --- a/training/update_apg_coefficients.py +++ b/training/update_apg_coefficients.py @@ -54,7 +54,45 @@ scale_APG = 1e21 scale_HV = 1000 s0hv_max = [None, 3.0, 1.3, 1.0, 0.9, 0.8] - +s0hv_apg_corr_min = [None, 0.96, 0.89, 0.89, 0.95, 0.80] +IPF_mapping = { + 2.36 : 2.36, + 2.40 : 2.43, # extra + 2.43 : 2.43, + 2.45 : 2.45, + 2.50 : 2.51, # extra + 2.51 : 2.51, + 2.52 : 2.52, + 2.53 : 2.53, + 2.60 : 2.60, + 2.70 : 2.71, # extra + 2.71 : 2.71, + 2.72 : 2.72, + 2.82 : 2.82, + 2.84 : 2.84, + 2.90 : 2.91, # extra + 2.91 : 2.91, + 3.10 : 3.10, + 3.20 : 3.31, # extra + 3.30 : 3.31, # extra + 3.31 : 3.31, + 3.40 : 3.52, # extra + 3.51 : 3.52, # extra + 3.52 : 3.52, + 3.61 : 3.61, +} + +def get_uid_mapping(uids): + uid_mapping = {} + for platform in ['S1A', 'S1B']: + ipf2uid = {float(uid.split('_')[-1]): uid for uid in uids if uid.startswith(platform)} + for dst_ipf in IPF_mapping: + src_ipf = IPF_mapping[dst_ipf] + if src_ipf in ipf2uid: + src_uid = ipf2uid[src_ipf] + dst_uid = '_'.join(src_uid.split('_')[:-1] + [f'{dst_ipf:04.2f}']) + uid_mapping[dst_uid] = src_uid + return uid_mapping def parse_run_experiment_args(): """ Parse input args for run_experiment_* scripts """ @@ -96,7 +134,7 @@ def parse_run_experiment_args(): swath_ids_skip = skip_swath_borders(swath_ids, skip=2) sigma0hv_s = [i * scale_HV for i in sigma0hv] apg_s = [i * scale_APG for i in apg] - A, Y = build_AY_matrix(swath_ids_skip, sigma0hv_s, apg_s, incang, s0hv_max) + A, Y = build_AY_matrix(swath_ids_skip, sigma0hv_s, apg_s, incang, s0hv_max, s0hv_apg_corr_min) if A is not None: ll['a'].append(A) ll['y'].append(Y) @@ -121,17 +159,19 @@ def parse_run_experiment_args(): plt.xlim([0, 6]) plt.ylim([0, 6]) plt.gca().set_aspect('equal') - plt.savefig(f'{uid}_{hv_name}_quality.png') + plt.savefig(f'{uid}_quality.png') plt.close() +uid_mapping = get_uid_mapping(uids) + p = safe_load(args.out_file) -for uid in B: - print('Save', uid) +for uid in uid_mapping: + print(f'Save {uid} ({uid_mapping[uid]})') p[uid] = dict( Y_SCALE = scale_HV, A_SCALE = scale_APG, - B = B[uid].tolist(), - RMSD = rmsd[uid], + B = B[uid_mapping[uid]].tolist(), + RMSD = rmsd[uid_mapping[uid]], ) with open(args.out_file, "w") as f: From 6380575c5a75151067d6e2ae82ee70649f35c6b7 Mon Sep 17 00:00:00 2001 From: akorosov Date: Mon, 9 Oct 2023 10:12:52 +0200 Subject: [PATCH 10/25] add algorithm to s1_correction.py --- s1denoise/scripts/s1_correction.py | 3 ++- s1denoise/tools.py | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/s1denoise/scripts/s1_correction.py b/s1denoise/scripts/s1_correction.py index 7a58540..d03a8e0 100755 --- a/s1denoise/scripts/s1_correction.py +++ b/s1denoise/scripts/s1_correction.py @@ -13,12 +13,13 @@ def parse_args(args): parser.add_argument('ifile', type=str, help='input Sentinel-1 file in SAFE format') parser.add_argument('ofile', type=str, help='output GeoTIFF file') parser.add_argument('-m', '--mask', help='Export land mask as a numpy file', action='store_true') + parser.add_argument('-a', '--algorithm', help='Name of the algorithm to use', type=str, default='NESRC') return parser.parse_args(args) if __name__ == '__main__': args = parse_args(sys.argv[1:]) - s1 = run_correction(args.ifile) + s1 = run_correction(args.ifile, algorithm=args.algorithm) s1.export(args.ofile, driver='GTiff') if args.mask: wm = s1.watermask() diff --git a/s1denoise/tools.py b/s1denoise/tools.py index 2429177..e664524 100644 --- a/s1denoise/tools.py +++ b/s1denoise/tools.py @@ -15,6 +15,7 @@ def run_correction(ifile, angular_scale_crpol=-0.025, angular_offset=34.5, output_dtype=np.float32, + algorithm='NERSC', **kwargs): """ Run thermal, textural and angular correction of input Sentinel-1 file @@ -54,7 +55,7 @@ def run_correction(ifile, parameters = s1.get_metadata(band_id='sigma0_%s' % pol) for i in ['dataType', 'PixelFunctionType', 'SourceBand', 'SourceFilename']: parameters.pop(i) - array = s1.remove_texture_noise(pol, **kwargs) + array = s1.remove_texture_noise(pol, algorithm=algorithm, **kwargs) array = 10 * np.log10(array) - scale[pol] * (inc - angular_offset) n.add_band(array=array.astype(output_dtype), parameters=parameters) n.set_metadata(s1.get_metadata()) From d1e6ec144a1788b03650f73ddd5d6e3b4eb5cc05 Mon Sep 17 00:00:00 2001 From: akorosov Date: Mon, 9 Oct 2023 11:30:25 +0200 Subject: [PATCH 11/25] fix bug with zero noise for early IPFs --- s1denoise/sentinel1image.py | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index abd0be9..47aa77e 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -1222,12 +1222,14 @@ def remove_texture_noise(self, polarization, window=3, weight=0.15, s0_min=0, re self.IPFversion = 3.1 sigma0 = self.get_raw_sigma0_full_size(polarization, min_dn=min_dn) nesz = self.get_nesz_full_size(polarization, algorithm) - sigma0 -= nesz - s0_offset = np.nanmean(nesz) - sigma0g = gaussian_filter(sigma0, window) - snr = sigma0g / nesz - sigma0o = (weight * sigma0g + snr * sigma0) / (weight + snr) + s0_offset + if s0_offset == 0: + sigma0o = sigma0 + else: + sigma0 -= nesz + sigma0g = gaussian_filter(sigma0, window) + snr = sigma0g / nesz + sigma0o = (weight * sigma0g + snr * sigma0) / (weight + snr) + s0_offset if remove_negative: sigma0o = fill_gaps(sigma0o, sigma0o <= s0_min) From 0b90c4b6dbb4fba8ec619c62dd929c4f0ceff3e4 Mon Sep 17 00:00:00 2001 From: akorosov Date: Mon, 4 Dec 2023 12:09:11 +0100 Subject: [PATCH 12/25] compute gtot instead of apg --- s1denoise/scripts/s1_correction.py | 2 +- s1denoise/sentinel1image.py | 37 +++++---- training/collect_apg_data.py | 113 ++++++++++++++++++---------- training/update_apg_coefficients.py | 50 ++++++------ validation/run_qm_batch.py | 3 +- 5 files changed, 127 insertions(+), 78 deletions(-) diff --git a/s1denoise/scripts/s1_correction.py b/s1denoise/scripts/s1_correction.py index d03a8e0..108076e 100755 --- a/s1denoise/scripts/s1_correction.py +++ b/s1denoise/scripts/s1_correction.py @@ -13,7 +13,7 @@ def parse_args(args): parser.add_argument('ifile', type=str, help='input Sentinel-1 file in SAFE format') parser.add_argument('ofile', type=str, help='output GeoTIFF file') parser.add_argument('-m', '--mask', help='Export land mask as a numpy file', action='store_true') - parser.add_argument('-a', '--algorithm', help='Name of the algorithm to use', type=str, default='NESRC') + parser.add_argument('-a', '--algorithm', help='Name of the algorithm to use', type=str, default='NERSC') return parser.parse_args(args) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index 47aa77e..449b5ca 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -216,7 +216,7 @@ def get_swath_id_vectors(self, polarization, line, pixel): swath_indices[v1][valid2] = iswath + 1 return swath_indices - def get_eap_rsl_vectors(self, polarization, line, pixel, min_size=3): + def get_eap_rsl_vectors(self, polarization, line, pixel, min_size=3, rsl_power=3./2.): """ Compute Antenna Pattern Gain APG=(1/EAP/RSL)**2 for each noise vectors """ eap_vectors = [np.zeros(p.size) + np.nan for p in pixel] rsl_vectors = [np.zeros(p.size) + np.nan for p in pixel] @@ -226,7 +226,7 @@ def get_eap_rsl_vectors(self, polarization, line, pixel, min_size=3): swath_name = f'{self.obsMode}{swid}' eap_interpolator = self.get_eap_interpolator(swath_name, polarization) ba_interpolator = self.get_boresight_angle_interpolator(polarization) - rsl_interpolator = self.get_range_spread_loss_interpolator(polarization) + rsl_interpolator = self.get_range_spread_loss_interpolator(polarization, rsl_power=rsl_power) for i, (l, p, swids) in enumerate(zip(line, pixel, swath_indices)): gpi = np.where(swids == swid)[0] if gpi.size > min_size: @@ -386,9 +386,9 @@ def get_boresight_angle_interpolator(self, polarization): boresight_angle_interpolator = RectBivariateSpline(yggp, xggp, boresight_map) return boresight_angle_interpolator - def get_range_spread_loss_interpolator(self, polarization): - """ Prepare interpolator for Range Spreading Loss. - It computes RSL for input x,y coordinates. + def get_range_spread_loss_interpolator(self, polarization, rsl_power=3./2.): + """ Prepare interpolator for Range Spreading Loss. It computes RSL for input x,y coordinates. + rsl_power : float power for RSL = (2 * Rref / time / C)^rsl_power """ geolocationGridPoint = self.import_geolocationGridPoint(polarization) @@ -397,11 +397,11 @@ def get_range_spread_loss_interpolator(self, polarization): referenceRange = float(self.annotationXML[polarization].getElementsByTagName( 'referenceRange')[0].childNodes[0].nodeValue) slantRangeTime = np.reshape(geolocationGridPoint['slantRangeTime'],(len(yggp),len(xggp))) - rangeSpreadingLoss = (referenceRange / slantRangeTime / SPEED_OF_LIGHT * 2)**(3./2.) + rangeSpreadingLoss = (referenceRange / slantRangeTime / SPEED_OF_LIGHT * 2)**rsl_power rsp_interpolator = RectBivariateSpline(yggp, xggp, rangeSpreadingLoss) return rsp_interpolator - def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip=4, min_valid_size=10): + def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip=4, min_valid_size=10, rsl_power=3./2.): """ Estimate shift in range noise LUT relative to antenna gain pattern and correct for it. @@ -414,7 +414,7 @@ def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip=4, mi swathBound = swathBounds[swath_name] eap_interpolator = self.get_eap_interpolator(swath_name, polarization) ba_interpolator = self.get_boresight_angle_interpolator(polarization) - rsp_interpolator = self.get_range_spread_loss_interpolator(polarization) + rsp_interpolator = self.get_range_spread_loss_interpolator(polarization, rsl_power=rsl_power) zipped = zip( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -573,23 +573,30 @@ def compute_rqm(self, s0, polarization, line, pixel, num_px=100, **kwargs): return q_all def get_range_quality_metric(self, polarization='HV', **kwargs): - """ Compute sigma0 with three methods (ESA, SHIFTED, NERSC), compute RQM for each sigma0 """ + """ Compute sigma0 with four methods (ESA, SHIFTED, NERSC, APG), compute RQM for each sigma0 """ line, pixel, noise = self.get_noise_range_vectors(polarization) cal_s0 = self.get_calibration_vectors(polarization, line, pixel) + + if self.IPFversion < 2.9: + scall_esa = [np.ones(p.size) for p in pixel] + else: + scall_esa = self.get_noise_azimuth_vectors(polarization, line, pixel) + nesz_esa = self.calibrate_noise_vectors(noise, cal_s0, scall_esa) scall = self.get_noise_azimuth_vectors(polarization, line, pixel) - nesz = self.calibrate_noise_vectors(noise, cal_s0, scall) noise_shifted = self.get_shifted_noise_vectors(polarization, line, pixel, noise) nesz_shifted = self.calibrate_noise_vectors(noise_shifted, cal_s0, scall) nesz_corrected = self.get_corrected_noise_vectors(polarization, line, pixel, nesz_shifted) + noise_apg = self.get_noise_apg_vectors(polarization, line, pixel) + nesz_apg = self.calibrate_noise_vectors(noise_apg, cal_s0, scall) sigma0 = self.get_raw_sigma0_vectors(polarization, line, pixel, cal_s0) - s0_esa = [s0 - n0 for (s0,n0) in zip(sigma0, nesz)] + s0_esa = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_esa)] s0_shift = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_shifted)] s0_nersc = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_corrected)] - q = [self.compute_rqm(s0, polarization, line, pixel, **kwargs) for s0 in [s0_esa, s0_shift, s0_nersc]] - - alg_names = ['ESA', 'SHIFT', 'NERSC'] + s0_apg = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_apg)] + q = [self.compute_rqm(s0, polarization, line, pixel) for s0 in [s0_esa, s0_shift, s0_nersc, s0_apg]] + alg_names = ['ESA', 'SHIFT', 'NERSC', 'APG'] var_names = ['RQM', 'AVG1', 'AVG2', 'STD1', 'STD2'] - q_all = {} + q_all = {'IPF': self.IPFversion} for swid in self.swath_ids[:-1]: swath_name = f'{self.obsMode}{swid}' for alg_i, alg_name in enumerate(alg_names): diff --git a/training/collect_apg_data.py b/training/collect_apg_data.py index e0405cf..925d27d 100755 --- a/training/collect_apg_data.py +++ b/training/collect_apg_data.py @@ -8,44 +8,79 @@ """ import argparse from collections import defaultdict +from datetime import datetime import os from pathlib import Path from bs4 import BeautifulSoup import numpy as np +from scipy.interpolate import InterpolatedUnivariateSpline from s1denoise import Sentinel1Image -def read_kproc(s1): - soup = BeautifulSoup(s1.annotationXML['HV'].toxml()) - kprop = {spp.swath.text: float(spp.processorscalingfactor.text) +def get_processor_scaling_factor(soup): + k_proc = {spp.swath.text: float(spp.processorscalingfactor.text) for spp in soup.find_all('swathprocparams')} - return kprop - -def get_pgpp_pgpa(s1): - soup = BeautifulSoup(s1.annotationXML['HV'].toxml()) - pgpp = soup.find_all('pgproductphase') - pgpa = soup.find_all('pgproductamplitude') - pha_dict = defaultdict(list) - amp_dict = defaultdict(list) - - for pg in pgpp: - pha_dict[pg.parent.parent.parent.swath.text].append(float(pg.text)) - for pg in pgpa: - amp_dict[pg.parent.parent.parent.swath.text].append(float(pg.text)) - return pha_dict, amp_dict - -def get_mean_pgpp_pgpa(s1): - pha_dict, amp_dict = get_pgpp_pgpa(s1) - pha_dict_avg = {i: np.mean(pha_dict[i]) for i in pha_dict} - amp_dict_avg = {i: np.mean(amp_dict[i]) for i in amp_dict} - return pha_dict_avg, amp_dict_avg + return k_proc + +def get_relative_azimuth_time(s1, polarization, line): + geolocationGridPoint = s1.import_geolocationGridPoint(polarization) + xggp = np.unique(geolocationGridPoint['pixel']) + yggp = np.unique(geolocationGridPoint['line']) + + azimuth_time = [ (t-s1.time_coverage_center).total_seconds() for t in geolocationGridPoint['azimuthTime'] ] + azimuth_time = np.reshape(azimuth_time, (len(yggp), len(xggp))) + at_interp = InterpolatedUnivariateSpline(yggp, azimuth_time[:,0]) + azimuth_time = at_interp(line) + return azimuth_time + +def get_pg_product(s1, soup, azimuth_time, pg_name='pgproductamplitude'): + pg = defaultdict(dict) + for pgpa in soup.find_all(pg_name): + pg[pgpa.parent.parent.parent.swath.text][pgpa.parent.azimuthtime.text] = float(pgpa.text) + + pg_swaths = {} + for swid in pg: + rel_az_time = np.array([ + (datetime.strptime(t, '%Y-%m-%dT%H:%M:%S.%f') - s1.time_coverage_center).total_seconds() + for t in pg[swid]]) + pgvec = np.array([pg[swid][i] for i in pg[swid]]) + sortIndex = np.argsort(rel_az_time) + pg_interp = InterpolatedUnivariateSpline(rel_az_time[sortIndex], pgvec[sortIndex], k=1) + pg_vec = pg_interp(azimuth_time) + pg_swaths[swid] = pg_vec + return pg_swaths + +def get_noise_power_correction_factor(s1, soup, azimuth_time): + npcf = defaultdict(dict) + for i in soup.find_all('noisepowercorrectionfactor'): + npcf[i.parent.swath.text][i.parent.azimuthtime.text] = float(i.text) + + npcf_swaths = {} + for swid in npcf: + rel_az_time = np.array([ + (datetime.strptime(t, '%Y-%m-%dT%H:%M:%S.%f') - s1.time_coverage_center).total_seconds() + for t in npcf[swid]]) + npcfvec = np.array([npcf[swid][i] for i in npcf[swid]]) + if npcfvec.size == 1: + npcf_vec = np.ones(azimuth_time.size) * npcfvec[0] + else: + sortIndex = np.argsort(rel_az_time) + npcf_interp = InterpolatedUnivariateSpline(rel_az_time[sortIndex], npcfvec[sortIndex], k=1) + npcf_vec = npcf_interp(azimuth_time) + npcf_swaths[swid] = npcf_vec + return npcf_swaths + +def get_noise_calibration_factor(s1): + eap_xml = s1.import_elevationAntennaPattern('HV') + noise_ca_fa = {i: eap_xml[i]['noiseCalibrationFactor'] for i in eap_xml} + return noise_ca_fa def parse_run_experiment_args(): """ Parse input args for run_experiment_* scripts """ parser = argparse.ArgumentParser(description='Process SAFE or ZIP files and collect APG related values') parser.add_argument('-o', '--out-dir', type=Path) - parser.add_argument('-f', '--force', action='store_true', help="Force overwrite existing output files") + parser.add_argument('-w', '--overwrite', action='store_true', help="Overwrite existing output files") parser.add_argument('-i', '--inp-files', type=Path, nargs='+') return parser.parse_args() @@ -57,7 +92,7 @@ def main(): for ifile in args.inp_files: ofile = os.path.join(args.out_dir, os.path.basename(ifile) + '_apg.npz') - if os.path.exists(ofile) and not args.force: + if os.path.exists(ofile) and not args.overwrite: print(ofile, 'exists') continue @@ -68,23 +103,24 @@ def main(): print('GDAL cannot open ', ifile) continue print('Process ', ifile) - + annotation_soup = BeautifulSoup(s1.annotationXML[polarization].toxml()) + k_proc = get_processor_scaling_factor(annotation_soup) line, pixel, noise = s1.get_noise_range_vectors(polarization) + az_time = get_relative_azimuth_time(s1, polarization, line) + pg_amplitude = get_pg_product(s1, annotation_soup, az_time, pg_name='pgproductamplitude') + noise_po_co_fa = get_noise_power_correction_factor(s1, annotation_soup, az_time) + noise_ca_fa = get_noise_calibration_factor(s1) + swath_ids = s1.get_swath_id_vectors(polarization, line, pixel) - eap, rsl = s1.get_eap_rsl_vectors(polarization, line, pixel) + eap, rsl = s1.get_eap_rsl_vectors(polarization, line, pixel, rsl_power=2.) ea_interpolator, ia_interpolator = s1.get_elevation_incidence_angle_interpolators(polarization) eleang, incang = s1.get_elevation_incidence_angle_vectors(ea_interpolator, ia_interpolator, line, pixel) cal_s0hv = s1.get_calibration_vectors(polarization, line, pixel) scall_hv = s1.get_noise_azimuth_vectors(polarization, line, pixel) dn_hv[dn_hv < 20] = np.nan - sigma0hv = s1.get_raw_sigma0_vectors_from_full_size(line, pixel, swath_ids, dn_hv) + dn_vectors = s1.get_raw_sigma0_vectors_from_full_size(line, pixel, swath_ids, dn_hv) - pgpp, pgpa = get_pgpp_pgpa(s1) - kproc = read_kproc(s1) - eap_xml = s1.import_elevationAntennaPattern('HV') - ncf = {i: eap_xml[i]['noiseCalibrationFactor'] for i in eap_xml} - acc = eap_xml['EW1']['absoluteCalibrationConstant'] ipf = s1.IPFversion np.savez( @@ -99,12 +135,11 @@ def main(): incang=incang, cal_s0hv=cal_s0hv, scall_hv=scall_hv, - sigma0hv=sigma0hv, - pgpp=pgpp, - pgpa=pgpa, - kproc=kproc, - ncf=ncf, - acc=acc, + dn_vectors=dn_vectors, + pg_amplitude=pg_amplitude, + noise_po_co_fa=noise_po_co_fa, + k_proc=k_proc, + noise_ca_fa=noise_ca_fa, ipf=ipf, ) diff --git a/training/update_apg_coefficients.py b/training/update_apg_coefficients.py index 2460cd0..e2a8f8a 100755 --- a/training/update_apg_coefficients.py +++ b/training/update_apg_coefficients.py @@ -28,7 +28,8 @@ from s1denoise.utils import skip_swath_borders, build_AY_matrix, solve from update_parameter_file import safe_load -array_names = ['line', +array_names = [ + 'line', 'pixel', 'noise', 'swath_ids', @@ -38,20 +39,19 @@ 'incang', 'cal_s0hv', 'scall_hv', - 'sigma0hv', + 'dn_vectors', ] item_names = [ - 'pgpp', - 'pgpa', - 'kproc', - 'ncf', - 'acc', - 'ipf' + 'pg_amplitude', + 'noise_po_co_fa', + 'k_proc', + 'noise_ca_fa', + 'ipf', ] polarization = 'HV' -scale_APG = 1e21 +scale_APG = 1e11 scale_HV = 1000 s0hv_max = [None, 3.0, 1.3, 1.0, 0.9, 0.8] s0hv_apg_corr_min = [None, 0.96, 0.89, 0.89, 0.95, 0.80] @@ -108,19 +108,27 @@ def parse_run_experiment_args(): l = defaultdict(list) for ifile in ifiles: print('Read', ifile) - try: - ds = np.load(ifile, allow_pickle=True) - d = {n: ds[n] for n in array_names} - except: - # TEMPORARY! - # skip if file is written at the moment - continue - + # load data + ds = np.load(ifile, allow_pickle=True) + d = {n: ds[n] for n in array_names} d.update({n: ds[n].item() for n in item_names}) - sigma0hv = d['sigma0hv'] ** 2 / d['cal_s0hv'] ** 2 - apg = (1 / d['eap'] / d['rsl']) ** 2 / d['cal_s0hv'] ** 2 * d['scall_hv'] + + # compute values + sigma0hv = d['dn_vectors'] ** 2 / d['cal_s0hv'] ** 2 + g_tots = [] + for i, (jjj, eee, rrr, sss, ccc) in enumerate(zip(d['swath_ids'], d['eap'], d['rsl'], d['scall_hv'], d['cal_s0hv'])): + g_tot = sss / eee ** 2 / rrr ** 2 / ccc ** 2 + for j in range(1,6): + gpi = jjj == j + key = f'EW{j}' + g_tot[gpi] *= d['k_proc'][key] + g_tot[gpi] *= d['noise_ca_fa'][key] + g_tot[gpi] *= d['pg_amplitude'][key][i] + g_tot[gpi] *= d['noise_po_co_fa'][key][i] + g_tots.append(g_tot) + l['ipf'].append(d['ipf']) - l['apg'].append(apg[1:-1]) + l['apg'].append(np.array(g_tots[1:-1])) l['sigma0hv'].append(sigma0hv[1:-1]) l['swath_ids'].append(d['swath_ids'][1:-1]) l['incang'].append(d['incang'][1:-1]) @@ -159,7 +167,7 @@ def parse_run_experiment_args(): plt.xlim([0, 6]) plt.ylim([0, 6]) plt.gca().set_aspect('equal') - plt.savefig(f'{uid}_quality.png') + plt.savefig(f'{uid}_quality_pg.png') plt.close() uid_mapping = get_uid_mapping(uids) diff --git a/validation/run_qm_batch.py b/validation/run_qm_batch.py index 2ebce45..b4737b8 100755 --- a/validation/run_qm_batch.py +++ b/validation/run_qm_batch.py @@ -16,7 +16,6 @@ from multiprocessing import Pool import numpy as np from s1denoise import Sentinel1Image -from sentinel1calval import Sentinel1CalVal out_dir = None pol = None @@ -87,7 +86,7 @@ def run_process(zipFile): if os.path.exists(out_fullname): print(f'{out_fullname} already exists.') else: - s1 = Sentinel1CalVal(zipFile) + s1 = Sentinel1Image(zipFile) func = getattr(s1, 'get_' + qm_name) res_qam = func(pol) print(res_qam) From e38fbb4bdd9ec1070b014b0499aeeadbc6ba3214 Mon Sep 17 00:00:00 2001 From: akorosov Date: Tue, 5 Dec 2023 09:58:54 +0100 Subject: [PATCH 13/25] stepwise changes with comments --- training/update_apg_coefficients.py | 42 ++++++++++++++++++++++++----- 1 file changed, 35 insertions(+), 7 deletions(-) diff --git a/training/update_apg_coefficients.py b/training/update_apg_coefficients.py index e2a8f8a..f4a7fd4 100755 --- a/training/update_apg_coefficients.py +++ b/training/update_apg_coefficients.py @@ -51,7 +51,7 @@ ] polarization = 'HV' -scale_APG = 1e11 +scale_APG = 1e21 scale_HV = 1000 s0hv_max = [None, 3.0, 1.3, 1.0, 0.9, 0.8] s0hv_apg_corr_min = [None, 0.96, 0.89, 0.89, 0.95, 0.80] @@ -104,12 +104,19 @@ def parse_run_experiment_args(): args = parse_run_experiment_args() ifiles = sorted(glob.glob(f'{args.inp_dir}/S1*_apg.npz')) +# good files +gfiles = [] l = defaultdict(list) for ifile in ifiles: print('Read', ifile) # load data ds = np.load(ifile, allow_pickle=True) + # 5: remove files withouth noise power correction factor (probably nrt-3h?) + # Fewer files in IPFs 2.36, 2.43, 2.45, 2.51, 2.52 (slightly better quality here), 2.53, 2.60, + #if len(ds["noise_po_co_fa"].item().keys()) == 0: + # continue + gfiles.append(ifile) d = {n: ds[n] for n in array_names} d.update({n: ds[n].item() for n in item_names}) @@ -117,14 +124,25 @@ def parse_run_experiment_args(): sigma0hv = d['dn_vectors'] ** 2 / d['cal_s0hv'] ** 2 g_tots = [] for i, (jjj, eee, rrr, sss, ccc) in enumerate(zip(d['swath_ids'], d['eap'], d['rsl'], d['scall_hv'], d['cal_s0hv'])): + # 0: original + # g_tot = sss / eee ** 2 / rrr ** 1.5 / ccc ** 2 + # 1: better correspondence g_tot = sss / eee ** 2 / rrr ** 2 / ccc ** 2 for j in range(1,6): gpi = jjj == j key = f'EW{j}' - g_tot[gpi] *= d['k_proc'][key] - g_tot[gpi] *= d['noise_ca_fa'][key] + # 2: add k_proc (same as #1) + #g_tot[gpi] *= d['k_proc'][key] + # 3: add noise calibration factor (2.36 and 2.51 became much worse / unusable, other IPFs - same) + #g_tot[gpi] *= d['noise_ca_fa'][key] + # 4. add pg product amplitude (2.52 became a bit better but 2.36 and 2.51 are still anusable) + #g_tot[gpi] *= d['pg_amplitude'][key][i] + # 6. add noise power correction factor + # (2.36 much better, other IPFs not much different from 5. Some a slightly better. Some are slightly worse.) + #g_tot[gpi] *= d['noise_po_co_fa'][key][i] + # 7. only pg product amplitude (without kproc, noise_ca_fa, noise_po_co_fa, filtering by noise_po_co_fa presence) + # (almost the same as #1; some IPFs are a bit better; some a abit worse; number of usable files - the same) g_tot[gpi] *= d['pg_amplitude'][key][i] - g_tot[gpi] *= d['noise_po_co_fa'][key][i] g_tots.append(g_tot) l['ipf'].append(d['ipf']) @@ -133,8 +151,18 @@ def parse_run_experiment_args(): l['swath_ids'].append(d['swath_ids'][1:-1]) l['incang'].append(d['incang'][1:-1]) +apg_all = np.hstack([np.hstack(i) for i in l['apg']]) +sigma0_all = np.hstack([np.hstack(i) for i in l['sigma0hv']]) + +nanmean_apg = np.nanmean(apg_all) +nanmean_s0hv = np.nanmean(sigma0_all) +scale_APG = 2 / nanmean_apg +print(f'\{nanmean_apg=} {nanmean_s0hv=} {scale_APG=}') +print(f'{(nanmean_apg * scale_APG)=} {(nanmean_s0hv * scale_HV)=}\n') + + ll = defaultdict(list) -for ipf, apg, sigma0hv, swath_ids, incang, ifile in zip(l['ipf'], l['apg'], l['sigma0hv'], l['swath_ids'], l['incang'], ifiles): +for ipf, apg, sigma0hv, swath_ids, incang, ifile in zip(l['ipf'], l['apg'], l['sigma0hv'], l['swath_ids'], l['incang'], gfiles): print('Build', ifile) name_parts = os.path.basename(ifile).split('_') platform, mode, resolution, pol = name_parts[0], name_parts[1], name_parts[2], name_parts[3] @@ -163,11 +191,11 @@ def parse_run_experiment_args(): Yrec = np.dot(A, B[uid]) plt.plot(Y.flat, Yrec, 'k.', alpha=0.1) plt.plot(Y.flat, Y.flat, 'r-') - plt.title(f'{uid} {rmsd[uid]:1.2f}') + plt.title(f'{uid} {uid_indices.size} {rmsd[uid]:1.2f}') plt.xlim([0, 6]) plt.ylim([0, 6]) plt.gca().set_aspect('equal') - plt.savefig(f'{uid}_quality_pg.png') + plt.savefig(f'{uid}_quality_pg7.png') plt.close() uid_mapping = get_uid_mapping(uids) From 5b79053b8c674df13b01f5bed90f4293eebb0af7 Mon Sep 17 00:00:00 2001 From: akorosov Date: Thu, 7 Dec 2023 10:46:46 +0100 Subject: [PATCH 14/25] major refactor: replace APG with TG; replace some import_ methods with cached_property --- s1denoise/denoising_parameters.json | 2 +- s1denoise/sentinel1image.py | 369 ++++++++++++++-------------- s1denoise/utils.py | 3 + training/update_apg_coefficients.py | 4 +- 4 files changed, 194 insertions(+), 184 deletions(-) diff --git a/s1denoise/denoising_parameters.json b/s1denoise/denoising_parameters.json index bbe7f79..1ae4864 100644 --- a/s1denoise/denoising_parameters.json +++ b/s1denoise/denoising_parameters.json @@ -1 +1 @@ -{"S1A_EW_GRDM_HV_NS_2.4": {"EW1": 1.2226739798419997, "EW2": 0.9615959470712395, "EW3": 1.0292391382243975, "EW4": 1.0065355608166793, "EW5": 0.9218665616511147}, "S1A_EW_GRDM_HV_NS_2.5": {"EW1": 1.2171388595679526, "EW2": 0.9536877350555699, "EW3": 1.015597864698578, "EW4": 0.9970741241853454, "EW5": 0.92307724873829}, "S1A_EW_GRDM_HV_NS_2.6": {"EW1": 1.1719745307602576, "EW2": 0.9163712571049173, "EW3": 0.9681532184092376, "EW4": 0.9430584692825155, "EW5": 0.8780597521669482}, "S1A_EW_GRDM_HV_NS_2.7": {"EW1": 1.398936536855681, "EW2": 0.9814810558391361, "EW3": 1.0515762397929336, "EW4": 1.0190944087059224, "EW5": 0.9595736050922239}, "S1A_EW_GRDM_HV_NS_2.8": {"EW1": 1.3043153896695185, "EW2": 0.9763390733024808, "EW3": 0.9975155379952487, "EW4": 0.9348512736439629, "EW5": 0.9625411730186109}, "S1A_EW_GRDM_HV_NS_2.9": {"EW1": 1.4807347002871454, "EW2": 1.0029777509123536, "EW3": 0.9874088104539668, "EW4": 1.057951324021166, "EW5": 1.0227437274453466}, "S1A_EW_GRDM_HV_PB_2.4": {"EW1": 7.830272905064692e-07, "EW2": 4.449220497638743e-06, "EW3": 4.718034189102612e-05, "EW4": 5.477365452456807e-06, "EW5": -3.1348551872009446e-06}, "S1A_EW_GRDM_HV_PB_2.5": {"EW1": 4.3866672637582335e-05, "EW2": 1.0796195201333851e-05, "EW3": 4.9065318157630286e-05, "EW4": 1.3722300950028815e-05, "EW5": -2.2557295998616656e-06}, "S1A_EW_GRDM_HV_PB_2.6": {"EW1": -3.819198068783059e-05, "EW2": 1.2193826271037744e-05, "EW3": 6.131842637362156e-05, "EW4": 7.392873409112185e-05, "EW5": 6.972248890083653e-05}, "S1A_EW_GRDM_HV_PB_2.7": {"EW1": -3.5621969376463706e-05, "EW2": -3.28432147836913e-05, "EW3": 9.087565093756112e-06, "EW4": 4.712064397346431e-06, "EW5": -6.272081842249038e-07}, "S1A_EW_GRDM_HV_PB_2.8": {"EW1": 0.00010123392382994249, "EW2": 1.4530929340856857e-05, "EW3": 3.552500456185288e-05, "EW4": 1.35067592097569e-05, "EW5": 2.6700987639279118e-05}, "S1A_EW_GRDM_HV_PB_2.9": {"EW1": 8.975356126433124e-05, "EW2": -3.2072923462756304e-05, "EW3": -4.2308784256238215e-06, "EW4": -9.741424762886609e-06, "EW5": 1.4381981407809117e-05}, "S1A_EW_GRDM_HV_ES_2.9": {"EW1": [216.22073753235657, 215.99164944375804, 215.7549889444315, 215.51057780219685, 215.25823708861273, 214.99778735394585, 214.72904880891673, 214.4518415130601, 214.1659855695176, 213.871301326056, 213.56760958207863, 213.25473180137493, 212.9324903303288, 212.6007086212813, 212.25921146072196, 211.9078252019562, 211.54637800187825, 211.17470006145228, 210.7926238694862, 210.39998444925988, 209.99661960755293, 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b/s1denoise/sentinel1image.py index 449b5ca..9b3098a 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -25,6 +25,7 @@ get_DOM_nodeValue, fill_gaps, cubic_hermite_interpolation, + parse_azimuth_time, ) SPEED_OF_LIGHT = 299792458. @@ -72,6 +73,7 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): noiseFiles = [fn for fn in zf.namelist() if 'annotation/calibration/noise-s1' in fn] for polarization in [txPol + 'H', txPol + 'V']: + # TODO: replace self.annotationXML[polarization].toxml() with raw XML self.annotationXML[polarization] = parseString( [zf.read(fn) for fn in annotationFiles if polarization.lower() in fn][0]) self.calibrationXML[polarization] = parseString( @@ -116,7 +118,7 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): if self.IPFversion < 2.43: print('\nERROR: IPF version of input image is lower than 2.43! ' 'Denoising vectors in annotation files are not qualified. ' - 'Only APG-based denoising can be performed\n') + 'Only TG-based denoising can be performed\n') elif 2.43 <= self.IPFversion < 2.53: print('\nWARNING: IPF version of input image is lower than 2.53! ' 'ESA default noise correction result might be wrong.\n') @@ -138,6 +140,86 @@ def scallopingGain(self, polarization='HV'): sg[subswathID] = self.get_subswathScallopingGain(polarization, subswathID) return sg + @cached_property + def swath_bounds(self): + """ Boundaries of blocks in each swath for each polarisation """ + names = { + 'azimuthTime' : parse_azimuth_time, + 'firstAzimuthLine' : int, + 'firstRangeSample' : int, + 'lastAzimuthLine' : int, + 'lastRangeSample' : int, + } + + swath_bounds = {} + for pol in self.pols: + soup = BeautifulSoup(self.annotationXML[pol].toxml(), features="xml") + swath_bounds[pol] = {} + for swathMerge in soup.find_all('swathMerge'): + swath_bounds[pol][swathMerge.swath.text] = defaultdict(list) + for swathBounds in swathMerge.find_all('swathBounds'): + for name in names: + swath_bounds[pol][swathMerge.swath.text][name].append(names[name](swathBounds.find(name).text)) + return swath_bounds + + @cached_property + def geolocation(self): + ''' Import geolocationGridPoint from annotation XML DOM ''' + geolocation_keys = { + 'azimuthTime': str, #lambda x : datetime.strptime(x, '%Y-%m-%dT%H:%M:%S.%f'), + 'slantRangeTime':float, + 'line': int, + 'pixel': int, + 'latitude':float, + 'longitude':float, + 'height':float, + 'incidenceAngle':float, + 'elevationAngle':float, + } + geolocation = {} + for pol in self.pols: + soup = BeautifulSoup(self.annotationXML[pol].toxml(), features="xml") + geolocation[pol] = defaultdict(list) + for p in soup.find_all('geolocationGridPoint'): + for c in p: + if c.name: + geolocation[pol][c.name].append(geolocation_keys[c.name](c.text)) + geolocation[pol]['line'] = np.unique(geolocation[pol]['line']) + geolocation[pol]['pixel'] = np.unique(geolocation[pol]['pixel']) + for i in geolocation[pol]: + if i not in ['line', 'pixel']: + geolocation[pol][i] = np.array(geolocation[pol][i]).reshape( + geolocation[pol]['line'].size, + geolocation[pol]['pixel'].size + ) + return geolocation + + @cached_property + def geolocation_relative_azimuth_time(self): + az_time = {} + for pol in self.pols: + az_time[pol] = list(map(parse_azimuth_time, self.geolocation[pol]['azimuthTime'].flat)) + az_time[pol] = [ (t-self.time_coverage_center).total_seconds() for t in az_time[pol]] + az_time[pol] = np.array(az_time[pol]).reshape(self.geolocation[pol]['azimuthTime'].shape) + return az_time + + @cached_property + def calibration(self): + calibration = {} + for pol in self.pols: + soup = BeautifulSoup(self.calibrationXML[pol].toxml(), features="xml") + calibration[pol] = defaultdict(list) + for cv in soup.find_all('calibrationVector'): + for n in cv: + if n.name: + calibration[pol][n.name].append(n.text) + calibration[pol]['azimuthTime'] = list(map(parse_azimuth_time, calibration[pol]['azimuthTime'])) + calibration[pol]['line'] = np.array(list(map(int, calibration[pol]['line']))) + calibration[pol]['pixel'] = np.array([list(map(int, p.split())) for p in calibration[pol]['pixel']]) + for key in ['sigmaNought', 'betaNought', 'gamma', 'dn']: + calibration[pol][key] = np.array([list(map(float, p.split())) for p in calibration[pol][key]]) + return calibration + def set_aux_data_dir(self): """ Set directory where aux calibration data is stored """ self.aux_data_dir = os.path.join(os.environ.get('XDG_DATA_HOME', os.path.expanduser('~')), @@ -199,7 +281,7 @@ def get_noise_range_vectors(self, polarization): def get_swath_id_vectors(self, polarization, line, pixel): swath_indices = [np.zeros(p.shape, int) for p in pixel] - swathBounds = self.import_swathBounds(polarization) + swathBounds = self.swath_bounds[polarization] for iswath, swath_name in enumerate(swathBounds): swathBound = swathBounds[swath_name] @@ -238,16 +320,42 @@ def get_eap_rsl_vectors(self, polarization, line, pixel, min_size=3, rsl_power=3 return eap_vectors, rsl_vectors - def get_apg_vectors(self, polarization, line, pixel, min_size=3): - """ Compute Antenna Pattern Gain APG=(1/EAP/RSL)**2 for each noise vectors """ - eap_vectors, rsl_vectors = self.get_eap_rsl_vectors(polarization, line, pixel, min_size=min_size) - apg_vectors = [(1 / eap / rsl) ** 2 for (eap,rsl) in zip(eap_vectors, rsl_vectors)] - return apg_vectors + def get_pg_product(self, polarization, pg_name='pgProductAmplitude'): + noiseRangeVector, _ = self.import_noiseVector(polarization) + azimuth_time = [(t - self.time_coverage_center).total_seconds() for t in noiseRangeVector['azimuthTime']] + annotation_soup = BeautifulSoup(self.annotationXML[polarization].toxml(), features="xml") + pg = defaultdict(dict) + for pgpa in annotation_soup.find_all(pg_name): + pg[pgpa.parent.parent.parent.swath.text][pgpa.parent.azimuthTime.text] = float(pgpa.text) + + pg_swaths = {} + for swid in pg: + rel_az_time = np.array([ + (datetime.strptime(t, '%Y-%m-%dT%H:%M:%S.%f') - self.time_coverage_center).total_seconds() + for t in pg[swid]]) + pgvec = np.array([pg[swid][i] for i in pg[swid]]) + sortIndex = np.argsort(rel_az_time) + pg_interp = InterpolatedUnivariateSpline(rel_az_time[sortIndex], pgvec[sortIndex], k=1) + pg_vec = pg_interp(azimuth_time) + pg_swaths[swid] = pg_vec + return pg_swaths + + def get_tg_vectors(self, polarization, line, pixel, min_size=3): + """ Compute Total Gain TG=Gtot*/(EAP/RSL)**2 for each noise vectors """ + eap_vectors, rsl_vectors = self.get_eap_rsl_vectors(polarization, line, pixel, min_size=min_size, rsl_power=2) + tg_vectors = [(1 / eap / rsl) ** 2 for (eap,rsl) in zip(eap_vectors, rsl_vectors)] + swath_ids = self.get_swath_id_vectors(polarization, line, pixel) + pg_swaths = self.get_pg_product(polarization) + for i, gtot in enumerate(tg_vectors): + for j in range(1,6): + gpi = swath_ids[i] == j + gtot[gpi] *= pg_swaths[f'EW{j}'][i] + return tg_vectors - def get_apg_scales_offsets(self): + def get_tg_scales_offsets(self): params = self.load_denoising_parameters_json() - apg_id = f'{os.path.basename(self.filename)[:16]}_APG_{self.IPFversion:04.2f}' - p = params[apg_id] + tg_id = f'{os.path.basename(self.filename)[:16]}_APG_{self.IPFversion:04.2f}' + p = params[tg_id] offsets = [] scales = [] for i in range(5): @@ -257,8 +365,7 @@ def get_apg_scales_offsets(self): def get_swath_interpolator(self, polarization, swath_name, line, pixel, z): """ Prepare interpolators for one swath """ - swathBounds = self.import_swathBounds(polarization) - swathBound = swathBounds[swath_name] + swathBound = self.swath_bounds[polarization][swath_name] swath_coords = ( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -290,32 +397,33 @@ def get_swath_interpolator(self, polarization, swath_name, line, pixel, z): z_interp2 = RectBivariateSpline(swath_lines, pix_vec_fr, np.array(z_vecs)) return z_interp2, swath_coords - def get_elevation_incidence_angle_interpolators(self, polarization): - geolocationGridPoint = self.import_geolocationGridPoint(polarization) - xggp = np.unique(geolocationGridPoint['pixel']) - yggp = np.unique(geolocationGridPoint['line']) - elevation_map = np.array(geolocationGridPoint['elevationAngle']).reshape(len(yggp), len(xggp)) - incidence_map = np.array(geolocationGridPoint['incidenceAngle']).reshape(len(yggp), len(xggp)) - elevation_angle_interpolator = RectBivariateSpline(yggp, xggp, elevation_map) - incidence_angle_interpolator = RectBivariateSpline(yggp, xggp, incidence_map) - return elevation_angle_interpolator, incidence_angle_interpolator + def get_elevation_incidence_angle_interpolators(self, p): + return ( + RectBivariateSpline( + self.geolocation[p]['line'], + self.geolocation[p]['pixel'], + self.geolocation[p]['elevationAngle']), + RectBivariateSpline( + self.geolocation[p]['line'], + self.geolocation[p]['pixel'], + self.geolocation[p]['incidenceAngle']) + ) def get_elevation_incidence_angle_vectors(self, ea_interpolator, ia_interpolator, line, pixel): ea = [ea_interpolator(l, p).flatten() for (l, p) in zip(line, pixel)] ia = [ia_interpolator(l, p).flatten() for (l, p) in zip(line, pixel)] return ea, ia - def get_calibration_vectors(self, polarization, line, pixel, name='sigmaNought'): + def get_calibration_vectors(self, pol, line, pixel, name='sigmaNought'): swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] - calibrationVector = self.import_calibrationVector(polarization) - s0line = np.array(calibrationVector['line']) - s0pixel = np.array(calibrationVector['pixel']) - sigma0 = np.array(calibrationVector[name]) + s0line = self.calibration[pol]['line'] + s0pixel = self.calibration[pol]['pixel'] + sigma0 = self.calibration[pol][name] sigma0_vecs = [np.zeros_like(p_vec) + np.nan for p_vec in pixel] for swath_name in swath_names: - sigma0interp, swath_coords = self.get_swath_interpolator(polarization, swath_name, s0line, s0pixel, sigma0) + sigma0interp, swath_coords = self.get_swath_interpolator(pol, swath_name, s0line, s0pixel, sigma0) for fal, lal, frs, lrs in zip(*swath_coords): valid1 = np.where((line >= fal) * (line <= lal))[0] @@ -352,18 +460,9 @@ def get_eap_interpolator(self, subswathID, polarization): eap_interpolator = InterpolatedUnivariateSpline(angleLUT, np.sqrt(amplitudeLUT)) return eap_interpolator - def get_boresight_angle_interpolator(self, polarization): - """ - Prepare interpolator for boresaight angles. - It computes BA for input x,y coordinates. - - """ - geolocationGridPoint = self.import_geolocationGridPoint(polarization) - xggp = np.unique(geolocationGridPoint['pixel']) - yggp = np.unique(geolocationGridPoint['line']) - elevation_map = np.array(geolocationGridPoint['elevationAngle']).reshape(len(yggp), len(xggp)) - - antennaPattern = self.import_antennaPattern(polarization) + def get_boresight_angle_interpolator(self, pol): + """ Prepare interpolator for boresight angles. It computes BA for input x,y coordinates. """ + antennaPattern = self.import_antennaPattern(pol) relativeAzimuthTime = [] for iSW in self.swath_ids: subswathID = '%s%s' % (self.obsMode, iSW) @@ -378,27 +477,23 @@ def get_boresight_angle_interpolator(self, polarization): relativeAzimuthTime = np.hstack(relativeAzimuthTime) rollAngle = np.hstack(rollAngle) rollAngleIntp = InterpolatedUnivariateSpline(relativeAzimuthTime[sortIndex], rollAngle[sortIndex]) - azimuthTime = [ (t-self.time_coverage_center).total_seconds() for t in geolocationGridPoint['azimuthTime'] ] - azimuthTime = np.reshape(azimuthTime, (len(yggp), len(xggp))) - roll_map = rollAngleIntp(azimuthTime) + roll_map = rollAngleIntp(self.geolocation_relative_azimuth_time[pol]) - boresight_map = elevation_map - roll_map - boresight_angle_interpolator = RectBivariateSpline(yggp, xggp, boresight_map) + boresight_map = self.geolocation[pol]['elevationAngle'] - roll_map + boresight_angle_interpolator = RectBivariateSpline( + self.geolocation[pol]['line'], self.geolocation[pol]['pixel'], boresight_map) return boresight_angle_interpolator - def get_range_spread_loss_interpolator(self, polarization, rsl_power=3./2.): + def get_range_spread_loss_interpolator(self, pol, rsl_power=3./2.): """ Prepare interpolator for Range Spreading Loss. It computes RSL for input x,y coordinates. rsl_power : float power for RSL = (2 * Rref / time / C)^rsl_power """ - geolocationGridPoint = self.import_geolocationGridPoint(polarization) - xggp = np.unique(geolocationGridPoint['pixel']) - yggp = np.unique(geolocationGridPoint['line']) - referenceRange = float(self.annotationXML[polarization].getElementsByTagName( + referenceRange = float(self.annotationXML[pol].getElementsByTagName( 'referenceRange')[0].childNodes[0].nodeValue) - slantRangeTime = np.reshape(geolocationGridPoint['slantRangeTime'],(len(yggp),len(xggp))) - rangeSpreadingLoss = (referenceRange / slantRangeTime / SPEED_OF_LIGHT * 2)**rsl_power - rsp_interpolator = RectBivariateSpline(yggp, xggp, rangeSpreadingLoss) + rangeSpreadingLoss = (referenceRange / self.geolocation[pol]['slantRangeTime'] / SPEED_OF_LIGHT * 2)**rsl_power + rsp_interpolator = RectBivariateSpline( + self.geolocation[pol]['line'], self.geolocation[pol]['pixel'], rangeSpreadingLoss) return rsp_interpolator def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip=4, min_valid_size=10, rsl_power=3./2.): @@ -407,11 +502,10 @@ def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip=4, mi """ noise_shifted = [np.zeros(p.size) for p in pixel] - swathBounds = self.import_swathBounds(polarization) # noise lut shift for swid in self.swath_ids: swath_name = f'{self.obsMode}{swid}' - swathBound = swathBounds[swath_name] + swathBound = self.swath_bounds[polarization][swath_name] eap_interpolator = self.get_eap_interpolator(swath_name, polarization) ba_interpolator = self.get_boresight_angle_interpolator(polarization) rsp_interpolator = self.get_range_spread_loss_interpolator(polarization, rsl_power=rsl_power) @@ -454,11 +548,10 @@ def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip=4, mi def get_corrected_noise_vectors(self, polarization, line, pixel, nesz, add_pb=True): """ Load scaling and offset coefficients from files and apply to input NESZ """ nesz_corrected = [np.zeros(p.size)+np.nan for p in pixel] - swathBounds = self.import_swathBounds(polarization) ns, pb = self.import_denoisingCoefficients(polarization)[:2] for swid in self.swath_ids: swath_name = f'{self.obsMode}{swid}' - swathBound = swathBounds[swath_name] + swathBound = self.swath_bounds[polarization][swath_name] zipped = zip( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -544,12 +637,11 @@ def get_ia_vectors_from_full_size(self, line, pixel, ia_fs): def compute_rqm(self, s0, polarization, line, pixel, num_px=100, **kwargs): """ Compute Range Quality Metric from the input sigma0 """ - swathBounds = self.import_swathBounds(polarization) q_all = {} for swid in self.swath_ids[:-1]: q_subswath = [] swath_name = f'{self.obsMode}{swid}' - swathBound = swathBounds[swath_name] + swathBound = self.swath_bounds[polarization][swath_name] zipped = zip( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -573,7 +665,7 @@ def compute_rqm(self, s0, polarization, line, pixel, num_px=100, **kwargs): return q_all def get_range_quality_metric(self, polarization='HV', **kwargs): - """ Compute sigma0 with four methods (ESA, SHIFTED, NERSC, APG), compute RQM for each sigma0 """ + """ Compute sigma0 with four methods (ESA, SHIFTED, NERSC, TG), compute RQM for each sigma0 """ line, pixel, noise = self.get_noise_range_vectors(polarization) cal_s0 = self.get_calibration_vectors(polarization, line, pixel) @@ -586,15 +678,15 @@ def get_range_quality_metric(self, polarization='HV', **kwargs): noise_shifted = self.get_shifted_noise_vectors(polarization, line, pixel, noise) nesz_shifted = self.calibrate_noise_vectors(noise_shifted, cal_s0, scall) nesz_corrected = self.get_corrected_noise_vectors(polarization, line, pixel, nesz_shifted) - noise_apg = self.get_noise_apg_vectors(polarization, line, pixel) - nesz_apg = self.calibrate_noise_vectors(noise_apg, cal_s0, scall) + noise_tg = self.get_noise_tg_vectors(polarization, line, pixel) + nesz_tg = self.calibrate_noise_vectors(noise_tg, cal_s0, scall) sigma0 = self.get_raw_sigma0_vectors(polarization, line, pixel, cal_s0) s0_esa = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_esa)] s0_shift = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_shifted)] s0_nersc = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_corrected)] - s0_apg = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_apg)] - q = [self.compute_rqm(s0, polarization, line, pixel) for s0 in [s0_esa, s0_shift, s0_nersc, s0_apg]] - alg_names = ['ESA', 'SHIFT', 'NERSC', 'APG'] + s0_tg = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_tg)] + q = [self.compute_rqm(s0, polarization, line, pixel) for s0 in [s0_esa, s0_shift, s0_nersc, s0_tg]] + alg_names = ['ESA', 'SHIFT', 'NERSC', 'TG'] var_names = ['RQM', 'AVG1', 'AVG2', 'STD1', 'STD2'] q_all = {'IPF': self.IPFversion} for swid in self.swath_ids[:-1]: @@ -608,7 +700,6 @@ def get_range_quality_metric(self, polarization='HV', **kwargs): def experiment_get_data(self, polarization, average_lines, zoom_step): """ Prepare data for noiseScaling and powerBalancing experiments """ crop = {'IW':400, 'EW':200}[self.obsMode] - swathBounds = self.import_swathBounds(polarization) line, pixel0, noise0 = self.get_noise_range_vectors(polarization) cal_s00 = self.get_calibration_vectors(polarization, line, pixel0) # zoom: @@ -623,7 +714,7 @@ def experiment_get_data(self, polarization, average_lines, zoom_step): sigma0 = self.get_raw_sigma0_vectors_from_full_size( polarization, line, pixel, sigma0_fs, average_lines=average_lines) - return line, pixel, sigma0, nesz, crop, swathBounds + return line, pixel, sigma0, nesz, crop, self.swath_bounds[polarization] def experiment_noiseScaling(self, polarization, average_lines=777, zoom_step=2): """ Compute noise scaling coefficients for each range noise line and save as NPZ """ @@ -796,25 +887,16 @@ def interp_nrv_full_size(self, z, line, pixel, polarization, power=1): z_fs[fal:lal+1, frs:lrs+1] = z_arr_fs return z_fs - def get_calibration_full_size(self, polarization, power=1): - """ Get calibration constant on full size matrix """ - calibrationVector = self.import_calibrationVector(polarization) - line = np.array(calibrationVector['line']) - pixel = np.array(calibrationVector['pixel']) - s0 = np.array(calibrationVector['sigmaNought']) - return self.interp_nrv_full_size(s0, line, pixel, polarization, power=power) - def get_corrected_nesz_full_size(self, polarization, nesz): """ Get corrected NESZ on full size matrix """ nesz_corrected = np.array(nesz) - swathBounds = self.import_swathBounds(polarization) ns, pb = self.import_denoisingCoefficients(polarization)[:2] for swid in self.swath_ids: swath_name = f'{self.obsMode}{swid}' # skip correction id NS/PB coeffs are not available (e.g. HH or VV) if swath_name not in ns: continue - swathBound = swathBounds[swath_name] + swathBound = self.swath_bounds[polarization][swath_name] zipped = zip( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -826,13 +908,17 @@ def get_corrected_nesz_full_size(self, polarization, nesz): nesz_corrected[fal:lal+1, frs:lrs+1] += pb[swath_name] return nesz_corrected - def get_raw_sigma0_full_size(self, polarization, min_dn=0): + def get_raw_sigma0_full_size(self, pol, min_dn=0): """ Read DN from input GeoTiff file and calibrate """ - src_filename = self.bands()[self.get_band_number(f'DN_{polarization}')]['SourceFilename'] + src_filename = self.bands()[self.get_band_number(f'DN_{pol}')]['SourceFilename'] ds = gdal.Open(src_filename) dn = ds.ReadAsArray() - sigma0_fs = dn.astype(float)**2 / self.get_calibration_full_size(polarization, power=2) + sigma0_fs = dn.astype(float)**2 / self.interp_nrv_full_size( + self.calibration[pol]['sigmaNought'], + self.calibration[pol]['line'], + self.calibration[pol]['pixel'], + pol, power=2) sigma0_fs[dn <= min_dn] = np.nan return sigma0_fs @@ -853,15 +939,15 @@ def export_noise_xml(self, polarization, output_path): tree.write(os.path.join(output_path, os.path.basename(crosspol_noise_file))) return crosspol_noise_file - def get_noise_apg_vectors(self, polarization, line, pixel): + def get_noise_tg_vectors(self, polarization, line, pixel): noise = [np.zeros_like(i) for i in pixel] - scales, offsets = self.get_apg_scales_offsets() - apg = self.get_apg_vectors(polarization, line, pixel) + scales, offsets = self.get_tg_scales_offsets() + gtot = self.get_tg_vectors(polarization, line, pixel) swath_ids = self.get_swath_id_vectors(polarization, line, pixel) - for i in range(len(apg)): + for i in range(len(gtot)): for j in range(1,6): gpi = swath_ids[i] == j - noise[i][gpi] = offsets[j-1] + apg[i][gpi] * scales[j-1] + noise[i][gpi] = offsets[j-1] + gtot[i][gpi] * scales[j-1] return noise def get_nesz_full_size(self, polarization, algorithm): @@ -870,9 +956,9 @@ def get_nesz_full_size(self, polarization, algorithm): if algorithm == 'NERSC': # NERSC correction of noise shift in range direction noise = self.get_shifted_noise_vectors(polarization, line, pixel, noise) - elif algorithm == 'NERSC_APG': - # APG-based noise vectors - noise = self.get_noise_apg_vectors(polarization, line, pixel) + elif algorithm == 'NERSC_TG': + # Total Gain - based noise vectors + noise = self.get_noise_tg_vectors(polarization, line, pixel) # noise calibration cal_s0 = self.get_calibration_vectors(polarization, line, pixel) nesz = [n / c**2 for (n,c) in zip(noise, cal_s0)] @@ -957,52 +1043,6 @@ def import_noiseVector(self, polarization): get_DOM_nodeValue(iList,[k],'int')) return noiseRangeVector, noiseAzimuthVector - def import_calibrationVector(self, polarization): - ''' Import calibration vectors from calibration annotation XML DOM ''' - calibrationVectorList = self.calibrationXML[polarization].getElementsByTagName( - 'calibrationVector') - calibrationVector = { 'azimuthTime':[], - 'line':[], - 'pixel':[], - 'sigmaNought':[], - 'betaNought':[], - 'gamma':[], - 'dn':[] } - for iList in calibrationVectorList: - for k in calibrationVector.keys(): - if k=='azimuthTime': - calibrationVector[k].append( - datetime.strptime(get_DOM_nodeValue(iList,[k]),'%Y-%m-%dT%H:%M:%S.%f')) - elif k in ['line', 'pixel']: - calibrationVector[k].append( - get_DOM_nodeValue(iList,[k],'int')) - else: - calibrationVector[k].append( - get_DOM_nodeValue(iList,[k],'float')) - return calibrationVector - - def import_swathBounds(self, polarization): - ''' Import swath bounds information from annotation XML DOM ''' - swathMergeList = self.annotationXML[polarization].getElementsByTagName('swathMerge') - swathBounds = { '%s%s' % (self.obsMode, li): - { 'azimuthTime':[], - 'firstAzimuthLine':[], - 'firstRangeSample':[], - 'lastAzimuthLine':[], - 'lastRangeSample':[] } - for li in self.swath_ids } - for iList1 in swathMergeList: - swath = get_DOM_nodeValue(iList1,['swath']) - swathBoundsList = iList1.getElementsByTagName('swathBounds') - for iList2 in swathBoundsList: - for k in swathBounds[swath].keys(): - if k=='azimuthTime': - swathBounds[swath][k].append( - datetime.strptime(get_DOM_nodeValue(iList2,[k]),'%Y-%m-%dT%H:%M:%S.%f')) - else: - swathBounds[swath][k].append(get_DOM_nodeValue(iList2,[k],'int')) - return swathBounds - def import_elevationAntennaPattern(self, polarization): ''' Import elevation antenna pattern from auxiliary calibration XML DOM ''' calParamsList = self.auxiliaryCalibrationXML.getElementsByTagName('calibrationParams') @@ -1029,32 +1069,6 @@ def import_elevationAntennaPattern(self, polarization): elevationAntennaPattern[swath][k] = get_DOM_nodeValue(iList,[k],'float') return elevationAntennaPattern - def import_geolocationGridPoint(self, polarization): - ''' Import geolocation grid point from annotation XML DOM ''' - geolocationGridPointList = self.annotationXML[polarization].getElementsByTagName( - 'geolocationGridPoint') - geolocationGridPoint = { 'azimuthTime':[], - 'slantRangeTime':[], - 'line':[], - 'pixel':[], - 'latitude':[], - 'longitude':[], - 'height':[], - 'incidenceAngle':[], - 'elevationAngle':[] } - for iList in geolocationGridPointList: - for k in geolocationGridPoint.keys(): - if k=='azimuthTime': - geolocationGridPoint[k].append( - datetime.strptime(get_DOM_nodeValue(iList,[k]),'%Y-%m-%dT%H:%M:%S.%f')) - elif k in ['line', 'pixel']: - geolocationGridPoint[k].append( - get_DOM_nodeValue(iList,[k],'int')) - else: - geolocationGridPoint[k].append( - get_DOM_nodeValue(iList,[k],'float')) - return geolocationGridPoint - def import_antennaPattern(self, polarization): ''' Import antenna pattern from annotation XML DOM ''' swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] @@ -1199,7 +1213,7 @@ def import_denoisingCoefficients(self, polarization, load_extra_scaling=False): return ( noiseScalingParameters, powerBalancingParameters, extraScalingParameters, noiseVarianceParameters ) - def remove_texture_noise(self, polarization, window=3, weight=0.15, s0_min=0, remove_negative=True, algorithm='NERSC', min_dn=0, **kwargs): + def remove_texture_noise(self, polarization, window=3, weight=0.1, s0_min=0, remove_negative=True, algorithm='NERSC', min_dn=0, **kwargs): """ Thermal noise removal followed by textural noise compensation using Method2 Method2 is implemented as a weighted average of sigma0 and sigma0 smoothed with @@ -1238,7 +1252,7 @@ def remove_texture_noise(self, polarization, window=3, weight=0.15, s0_min=0, re snr = sigma0g / nesz sigma0o = (weight * sigma0g + snr * sigma0) / (weight + snr) + s0_offset - if remove_negative: + if remove_negative and np.nanmin(sigma0o) < 0: sigma0o = fill_gaps(sigma0o, sigma0o <= s0_min) return sigma0o @@ -1246,9 +1260,8 @@ def remove_texture_noise(self, polarization, window=3, weight=0.15, s0_min=0, re def subswathIndexMap(self, polarization): ''' Convert subswath indices into full grid pixels ''' subswathIndexMap = np.zeros(self.shape(), dtype=np.uint8) - swathBounds = self.import_swathBounds(polarization) for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): - swathBound = swathBounds['%s%s' % (self.obsMode, iSW)] + swathBound = self.swath_bounds[polarization]['%s%s' % (self.obsMode, iSW)] zipped = zip(swathBound['firstAzimuthLine'], swathBound['firstRangeSample'], swathBound['lastAzimuthLine'], @@ -1285,7 +1298,7 @@ def import_azimuthAntennaElementPattern(self, polarization): def subswathCenterSampleIndex(self, polarization): ''' Range center pixel indices along azimuth for each subswath ''' - swathBounds = self.import_swathBounds(polarization) + swathBounds = self.swath_bounds[polarization] subswathCenterSampleIndex = {} for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): subswathID = '%s%s' % (self.obsMode, iSW) @@ -1297,20 +1310,14 @@ def subswathCenterSampleIndex(self, polarization): np.sum(midPixelIndices * numberOfLines) / np.sum(numberOfLines) )) return subswathCenterSampleIndex - def geolocationGridPointInterpolator(self, polarization, itemName): + def geolocationGridPointInterpolator(self, pol, itemName): ''' Generate interpolator for items in geolocation grid point list ''' - geolocationGridPoint = self.import_geolocationGridPoint(polarization) - if itemName not in geolocationGridPoint.keys(): - raise ValueError('%s is not in the geolocationGridPoint list.' % itemName) - x = np.unique(geolocationGridPoint['pixel']) - y = np.unique(geolocationGridPoint['line']) - if itemName=='azimuthTime': - z = [ (t-self.time_coverage_center).total_seconds() - for t in geolocationGridPoint['azimuthTime'] ] - z = np.reshape(z,(len(y),len(x))) + if itemName == 'azimuthTime': + z = self.geolocation_relative_azimuth_time[pol] else: - z = np.reshape(geolocationGridPoint[itemName],(len(y),len(x))) - interpolator = RectBivariateSpline(y, x, z) + z = self.geolocation[pol][itemName] + interpolator = RectBivariateSpline( + self.geolocation[pol]['line'], self.geolocation[pol]['pixel'], z) return interpolator def azimuthFmRateAtGivenTime(self, polarization, relativeAzimuthTime, slantRangeTime): @@ -1337,13 +1344,13 @@ def azimuthFmRateAtGivenTime(self, polarization, relativeAzimuthTime, slantRange def import_azimuthFmRate(self, polarization): ''' Import azimuth frequency modulation rate from annotation XML DOM ''' - soup = BeautifulSoup(self.annotationXML[polarization].toxml(), "lxml") + soup = BeautifulSoup(self.annotationXML[polarization].toxml(), features="xml") azimuthFmRate = defaultdict(list) - for afmr in soup.find_all('azimuthfmrate'): - azimuthFmRate['azimuthTime'].append(datetime.strptime(afmr.azimuthtime.text, '%Y-%m-%dT%H:%M:%S.%f')) + for afmr in soup.find_all('azimuthFmRate'): + azimuthFmRate['azimuthTime'].append(datetime.strptime(afmr.azimuthTime.text, '%Y-%m-%dT%H:%M:%S.%f')) azimuthFmRate['t0'].append(float(afmr.t0.text)) - if 'azimuthfmratepolynomial' in afmr.decode(): - afmrp = list(map(float,afmr.azimuthfmratepolynomial.text.split(' '))) + if 'azimuthFmRatePolynomial' in afmr.decode(): + afmrp = list(map(float, afmr.azimuthFmRatePolynomial.text.split(' '))) elif 'c0' in afmr.decode() and 'c1' in afmr.decode() and 'c2' in afmr.decode(): afmrp = [float(afmr.c0.text), float(afmr.c1.text), float(afmr.c2.text)] azimuthFmRate['azimuthFmRatePolynomial'].append(afmrp) diff --git a/s1denoise/utils.py b/s1denoise/utils.py index faf6910..ed5949f 100644 --- a/s1denoise/utils.py +++ b/s1denoise/utils.py @@ -1,4 +1,5 @@ from collections import defaultdict +from datetime import datetime import numpy as np from scipy.stats import pearsonr @@ -148,3 +149,5 @@ def solve(A, Y): Y_rec = np.dot(A, B[0]) rmsd = np.sqrt(np.mean((Y - Y_rec)**2)) return B[0].flatten(), rmsd + +parse_azimuth_time = lambda x : datetime.strptime(x, '%Y-%m-%dT%H:%M:%S.%f') diff --git a/training/update_apg_coefficients.py b/training/update_apg_coefficients.py index f4a7fd4..e38fe28 100755 --- a/training/update_apg_coefficients.py +++ b/training/update_apg_coefficients.py @@ -157,8 +157,8 @@ def parse_run_experiment_args(): nanmean_apg = np.nanmean(apg_all) nanmean_s0hv = np.nanmean(sigma0_all) scale_APG = 2 / nanmean_apg -print(f'\{nanmean_apg=} {nanmean_s0hv=} {scale_APG=}') -print(f'{(nanmean_apg * scale_APG)=} {(nanmean_s0hv * scale_HV)=}\n') +print(f'\{nanmean_apg} {nanmean_s0hv} {scale_APG}') +print(f'{(nanmean_apg * scale_APG)} {(nanmean_s0hv * scale_HV)}\n') ll = defaultdict(list) From da7a7023c4bef31e20d36d1a9d756a03d49ed4d9 Mon Sep 17 00:00:00 2001 From: akorosov Date: Fri, 8 Dec 2023 14:53:15 +0100 Subject: [PATCH 15/25] refactor import_noiseVector --- s1denoise/sentinel1image.py | 173 ++++++++++++++---------------------- 1 file changed, 69 insertions(+), 104 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index 9b3098a..1cb4b0a 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -8,7 +8,7 @@ import xml.etree.ElementTree as ET from xml.dom.minidom import parse, parseString import zipfile -from functools import cached_property +from functools import cached_property, cache from nansat import Nansat import numpy as np @@ -78,8 +78,8 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): [zf.read(fn) for fn in annotationFiles if polarization.lower() in fn][0]) self.calibrationXML[polarization] = parseString( [zf.read(fn) for fn in calibrationFiles if polarization.lower() in fn][0]) - self.noiseXML[polarization] = parseString( - [zf.read(fn) for fn in noiseFiles if polarization.lower() in fn][0]) + self.noiseXML[polarization] = BeautifulSoup( + [zf.read(fn) for fn in noiseFiles if polarization.lower() in fn][0], features="xml") self.manifestXML = parseString(zf.read([fn for fn in zf.namelist() if 'manifest.safe' in fn][0])) else: @@ -104,7 +104,7 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): for fn in noiseFiles: if polarization.lower() in fn: with open(fn) as ff: - self.noiseXML[polarization] = parseString(ff.read()) + self.noiseXML[polarization] = BeautifulSoup(ff.read(), features="xml") with open(glob.glob(self.filename+'/manifest.safe')[0]) as ff: self.manifestXML = parseString(ff.read()) @@ -220,6 +220,47 @@ def calibration(self): calibration[pol][key] = np.array([list(map(float, p.split())) for p in calibration[pol][key]]) return calibration + @cache + def noise_range(self, pol): + if self.IPFversion < 2.9: + noiseRangeVectorName = 'noiseVector' + noiseLutName = 'noiseLut' + else: + noiseRangeVectorName = 'noiseRangeVector' + noiseLutName = 'noiseRangeLut' + noise_range = defaultdict(list) + #soup = BeautifulSoup(self.noiseXML[pol].toxml(), features="xml") + for noiseVector in self.noiseXML[pol].find_all(noiseRangeVectorName): + noise_range['azimuthTime'].append(parse_azimuth_time(noiseVector.azimuthTime.text)) + noise_range['line'].append(int(noiseVector.line.text)) + noise_range['pixel'].append(np.array([int(i) for i in noiseVector.pixel.text.split()])) + noise_range['noise'].append(np.array([float(i) for i in noiseVector.find(noiseLutName).text.split()])) + noise_range['line'] = np.array(noise_range['line']) + return noise_range + + @cache + def noise_azimuth(self, pol): + noise_azimuth = {f'{self.obsMode}{swid}':defaultdict(list) for swid in self.swath_ids} + if self.IPFversion < 2.9: + for swid in self.swath_ids: + noise_azimuth[f'{self.obsMode}{swid}'] = dict( + firstAzimuthLine = [0], + firstRangeSample = [0], + lastAzimuthLine = [self.shape()[0]-1], + lastRangeSample = [self.shape()[1]-1], + line = [np.array([0, self.shape()[0]-1])], + noise = [np.array([1.0, 1.0])]) + else: + int_names = ['firstAzimuthLine', 'firstRangeSample', 'lastAzimuthLine', 'lastRangeSample'] + #soup = BeautifulSoup(self.noiseXML[pol].toxml(), features="xml") + for noiseAzimuthVector in self.noiseXML[pol].find_all('noiseAzimuthVector'): + swath = noiseAzimuthVector.swath.text + for int_name in int_names: + noise_azimuth[swath][int_name].append(int(noiseAzimuthVector.find(int_name).text)) + noise_azimuth[swath]['line'].append(np.array([int(i) for i in noiseAzimuthVector.line.text.split()])) + noise_azimuth[swath]['noise'].append(np.array([float(i) for i in noiseAzimuthVector.noiseAzimuthLut.text.split()])) + return noise_azimuth + def set_aux_data_dir(self): """ Set directory where aux calibration data is stored """ self.aux_data_dir = os.path.join(os.environ.get('XDG_DATA_HOME', os.path.expanduser('~')), @@ -262,22 +303,9 @@ def get_remote_url(api_url): # the best found AUX-CAL file is copied to the filename from manifest shutil.copytree(download_file.rstrip('.zip'), output_dir) - def get_noise_range_vectors(self, polarization): + def get_noise_range_vectors(self, pol): """ Get range noise from XML files and return noise, pixels and lines for non-zero elems""" - noiseRangeVector, noiseAzimuthVector = self.import_noiseVector(polarization) - line = np.array(noiseRangeVector['line']) - pixel = [] - noise = [] - - for pix, n in zip(noiseRangeVector['pixel'], noiseRangeVector['noiseRangeLut']): - n = np.array(n) - noise.append(n) - pixel.append(np.array(pix)) - - if self.IPFversion <= 2.43: - noise = [np.zeros_like(i) for i in noise] - - return line, pixel, noise + return self.noise_range(pol)['line'], self.noise_range(pol)['pixel'], self.noise_range(pol)['noise'] def get_swath_id_vectors(self, polarization, line, pixel): swath_indices = [np.zeros(p.shape, int) for p in pixel] @@ -320,10 +348,10 @@ def get_eap_rsl_vectors(self, polarization, line, pixel, min_size=3, rsl_power=3 return eap_vectors, rsl_vectors - def get_pg_product(self, polarization, pg_name='pgProductAmplitude'): - noiseRangeVector, _ = self.import_noiseVector(polarization) - azimuth_time = [(t - self.time_coverage_center).total_seconds() for t in noiseRangeVector['azimuthTime']] - annotation_soup = BeautifulSoup(self.annotationXML[polarization].toxml(), features="xml") + def get_pg_product(self, pol, pg_name='pgProductAmplitude'): + azimuth_time = [(t - self.time_coverage_center).total_seconds() + for t in self.noise_range(pol)['azimuthTime']] + annotation_soup = BeautifulSoup(self.annotationXML[pol].toxml(), features="xml") pg = defaultdict(dict) for pgpa in annotation_soup.find_all(pg_name): pg[pgpa.parent.parent.parent.swath.text][pgpa.parent.azimuthTime.text] = float(pgpa.text) @@ -980,69 +1008,6 @@ def remove_thermal_noise(self, polarization, algorithm='NERSC', remove_negative= sigma0 = fill_gaps(sigma0, sigma0 <= 0) return sigma0 - def import_noiseVector(self, polarization): - ''' Import noise vectors from noise annotation XML DOM ''' - noiseRangeVector = { 'azimuthTime':[], - 'line':[], - 'pixel':[], - 'noiseRangeLut':[] } - noiseAzimuthVector = { '%s%s' % (self.obsMode, li): - { 'firstAzimuthLine':[], - 'firstRangeSample':[], - 'lastAzimuthLine':[], - 'lastRangeSample':[], - 'line':[], - 'noiseAzimuthLut':[] } - for li in self.swath_ids } - # ESA changed the noise vector structure from IPFv 2.9 to include azimuth variation - if self.IPFversion < 2.9: - noiseVectorList = self.noiseXML[polarization].getElementsByTagName('noiseVector') - for iList in noiseVectorList: - noiseRangeVector['azimuthTime'].append( - datetime.strptime(get_DOM_nodeValue(iList,['azimuthTime']), - '%Y-%m-%dT%H:%M:%S.%f')) - noiseRangeVector['line'].append( - get_DOM_nodeValue(iList,['line'],'int')) - noiseRangeVector['pixel'].append( - get_DOM_nodeValue(iList,['pixel'],'int')) - # To keep consistency, noiseLut is stored as noiseRangeLut - noiseRangeVector['noiseRangeLut'].append( - get_DOM_nodeValue(iList,['noiseLut'],'float')) - for iSW in self.swath_ids: - swath = self.obsMode + str(iSW) - noiseAzimuthVector[swath]['firstAzimuthLine'].append(0) - noiseAzimuthVector[swath]['firstRangeSample'].append(0) - noiseAzimuthVector[swath]['lastAzimuthLine'].append(self.shape()[0]-1) - noiseAzimuthVector[swath]['lastRangeSample'].append(self.shape()[1]-1) - noiseAzimuthVector[swath]['line'].append([0, self.shape()[0]-1]) - # noiseAzimuthLut is filled with a vector of ones - noiseAzimuthVector[swath]['noiseAzimuthLut'].append([1.0, 1.0]) - elif self.IPFversion >= 2.9: - noiseRangeVectorList = self.noiseXML[polarization].getElementsByTagName( - 'noiseRangeVector') - for iList in noiseRangeVectorList: - noiseRangeVector['azimuthTime'].append( - datetime.strptime(get_DOM_nodeValue(iList,['azimuthTime']), - '%Y-%m-%dT%H:%M:%S.%f')) - noiseRangeVector['line'].append( - get_DOM_nodeValue(iList,['line'],'int')) - noiseRangeVector['pixel'].append( - get_DOM_nodeValue(iList,['pixel'],'int')) - noiseRangeVector['noiseRangeLut'].append( - get_DOM_nodeValue(iList,['noiseRangeLut'],'float')) - noiseAzimuthVectorList = self.noiseXML[polarization].getElementsByTagName( - 'noiseAzimuthVector') - for iList in noiseAzimuthVectorList: - swath = get_DOM_nodeValue(iList,['swath'],'str') - for k in noiseAzimuthVector[swath].keys(): - if k=='noiseAzimuthLut': - noiseAzimuthVector[swath][k].append( - get_DOM_nodeValue(iList,[k],'float')) - else: - noiseAzimuthVector[swath][k].append( - get_DOM_nodeValue(iList,[k],'int')) - return noiseRangeVector, noiseAzimuthVector - def import_elevationAntennaPattern(self, polarization): ''' Import elevation antenna pattern from auxiliary calibration XML DOM ''' calParamsList = self.auxiliaryCalibrationXML.getElementsByTagName('calibrationParams') @@ -1443,28 +1408,28 @@ def get_subswathScallopingGain(self, polarization, subswathID): scallopingGain = 1. / 10**(burstAAEP/10.) return scallopingGain - def get_noise_azimuth_vectors(self, polarization, line, pixel): + def get_noise_azimuth_vectors(self, pol, line, pixel): """ Interpolate scalloping noise from XML files to input line/pixel coords """ scall = [np.zeros(p.size) for p in pixel] if self.IPFversion < 2.9: - swath_idvs = self.get_swath_id_vectors(polarization, line, pixel) + swath_idvs = self.get_swath_id_vectors(pol, line, pixel) for i, l in enumerate(line): for j in range(1,6): gpi = swath_idvs[i] == j scall[i][gpi] = self.scallopingGain[f'EW{j}'][l] return scall - noiseRangeVector, noiseAzimuthVector = self.import_noiseVector(polarization) + noiseAzimuthVector = self.noise_azimuth(pol) for iSW in self.swath_ids: - subswathID = '%s%s' % (self.obsMode, iSW) - numberOfBlocks = len(noiseAzimuthVector[subswathID]['firstAzimuthLine']) + swath = f'{self.obsMode}{iSW}' + numberOfBlocks = len(noiseAzimuthVector[swath]['firstAzimuthLine']) for iBlk in range(numberOfBlocks): - frs = noiseAzimuthVector[subswathID]['firstRangeSample'][iBlk] - lrs = noiseAzimuthVector[subswathID]['lastRangeSample'][iBlk] - fal = noiseAzimuthVector[subswathID]['firstAzimuthLine'][iBlk] - lal = noiseAzimuthVector[subswathID]['lastAzimuthLine'][iBlk] - y = np.array(noiseAzimuthVector[subswathID]['line'][iBlk]) - z = np.array(noiseAzimuthVector[subswathID]['noiseAzimuthLut'][iBlk]) + frs = noiseAzimuthVector[swath]['firstRangeSample'][iBlk] + lrs = noiseAzimuthVector[swath]['lastRangeSample'][iBlk] + fal = noiseAzimuthVector[swath]['firstAzimuthLine'][iBlk] + lal = noiseAzimuthVector[swath]['lastAzimuthLine'][iBlk] + y = np.array(noiseAzimuthVector[swath]['line'][iBlk]) + z = np.array(noiseAzimuthVector[swath]['noise'][iBlk]) if y.size > 1: nav_interp = InterpolatedUnivariateSpline(y, z, k=1) else: @@ -1476,21 +1441,21 @@ def get_noise_azimuth_vectors(self, polarization, line, pixel): scall[line_i][pixel_gpi] = nav_interp(line[line_i]) return scall - def get_scalloping_full_size(self, polarization): + def get_scalloping_full_size(self, pol): """ Interpolate noise azimuth vector to full resolution for all blocks """ scall_fs = np.zeros(self.shape()) if self.IPFversion < 2.9: - subswathIndexMap = self.subswathIndexMap(polarization) + subswathIndexMap = self.subswathIndexMap(pol) for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): subswathID = '%s%s' % (self.obsMode, iSW) scallopingGain = self.scallopingGain[subswathID] # assign computed scalloping gain into each subswath valid = (subswathIndexMap==iSW) scall_fs[valid] = ( - scallopingGain[:,np.newaxis] * np.ones((1,self.shape()[1])))[valid] + scallopingGain[:, np.newaxis] * np.ones((1,self.shape()[1])))[valid] return scall_fs - noiseRangeVector, noiseAzimuthVector = self.import_noiseVector(polarization) + noiseAzimuthVector = self.noise_azimuth(pol) swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] for swath_name in swath_names: nav = noiseAzimuthVector[swath_name] @@ -1500,10 +1465,10 @@ def get_scalloping_full_size(self, polarization): nav['firstRangeSample'], nav['lastRangeSample'], nav['line'], - nav['noiseAzimuthLut'], + nav['noise'], ) for fal, lal, frs, lrs, y, z in zipped: - if isinstance(y, list): + if isinstance(y, (list, np.ndarray)): nav_interp = InterpolatedUnivariateSpline(y, z, k=1) else: nav_interp = lambda x: z @@ -1511,4 +1476,4 @@ def get_scalloping_full_size(self, polarization): z_vec_fr = nav_interp(lin_vec_fr) z_arr = np.repeat([z_vec_fr], (lrs-frs+1), axis=0).T scall_fs[fal:lal+1, frs:lrs+1] = z_arr - return scall_fs \ No newline at end of file + return scall_fs From c095ac01a818142723aa684f74a74b55857996b8 Mon Sep 17 00:00:00 2001 From: akorosov Date: Fri, 8 Dec 2023 16:14:25 +0100 Subject: [PATCH 16/25] refactor polarization and line, pixel --- s1denoise/sentinel1image.py | 315 +++++++++++++++++++----------------- 1 file changed, 168 insertions(+), 147 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index 1cb4b0a..89340d6 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -72,14 +72,14 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): if 'annotation/calibration/calibration-s1' in fn] noiseFiles = [fn for fn in zf.namelist() if 'annotation/calibration/noise-s1' in fn] - for polarization in [txPol + 'H', txPol + 'V']: - # TODO: replace self.annotationXML[polarization].toxml() with raw XML - self.annotationXML[polarization] = parseString( - [zf.read(fn) for fn in annotationFiles if polarization.lower() in fn][0]) - self.calibrationXML[polarization] = parseString( - [zf.read(fn) for fn in calibrationFiles if polarization.lower() in fn][0]) - self.noiseXML[polarization] = BeautifulSoup( - [zf.read(fn) for fn in noiseFiles if polarization.lower() in fn][0], features="xml") + for pol in [txPol + 'H', txPol + 'V']: + # TODO: replace self.annotationXML[pol].toxml() with raw XML + self.annotationXML[pol] = parseString( + [zf.read(fn) for fn in annotationFiles if pol.lower() in fn][0]) + self.calibrationXML[pol] = parseString( + [zf.read(fn) for fn in calibrationFiles if pol.lower() in fn][0]) + self.noiseXML[pol] = BeautifulSoup( + [zf.read(fn) for fn in noiseFiles if pol.lower() in fn][0], features="xml") self.manifestXML = parseString(zf.read([fn for fn in zf.namelist() if 'manifest.safe' in fn][0])) else: @@ -89,22 +89,22 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): noiseFiles = [fn for fn in glob.glob(self.filename+'/annotation/calibration/*') if 'noise-s1' in fn] - for polarization in [txPol + 'H', txPol + 'V']: + for pol in [txPol + 'H', txPol + 'V']: for fn in annotationFiles: - if polarization.lower() in fn: + if pol.lower() in fn: with open(fn) as ff: - self.annotationXML[polarization] = parseString(ff.read()) + self.annotationXML[pol] = parseString(ff.read()) for fn in calibrationFiles: - if polarization.lower() in fn: + if pol.lower() in fn: with open(fn) as ff: - self.calibrationXML[polarization] = parseString(ff.read()) + self.calibrationXML[pol] = parseString(ff.read()) for fn in noiseFiles: - if polarization.lower() in fn: + if pol.lower() in fn: with open(fn) as ff: - self.noiseXML[polarization] = BeautifulSoup(ff.read(), features="xml") + self.noiseXML[pol] = BeautifulSoup(ff.read(), features="xml") with open(glob.glob(self.filename+'/manifest.safe')[0]) as ff: self.manifestXML = parseString(ff.read()) @@ -133,11 +133,11 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): self.auxiliaryCalibrationXML = parse(self.auxiliaryCalibration_file) @cached_property - def scallopingGain(self, polarization='HV'): + def scallopingGain(self, pol='HV'): sg = {} for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): subswathID = '%s%s' % (self.obsMode, iSW) - sg[subswathID] = self.get_subswathScallopingGain(polarization, subswathID) + sg[subswathID] = self.get_subswathScallopingGain(pol, subswathID) return sg @cached_property @@ -303,13 +303,11 @@ def get_remote_url(api_url): # the best found AUX-CAL file is copied to the filename from manifest shutil.copytree(download_file.rstrip('.zip'), output_dir) - def get_noise_range_vectors(self, pol): - """ Get range noise from XML files and return noise, pixels and lines for non-zero elems""" - return self.noise_range(pol)['line'], self.noise_range(pol)['pixel'], self.noise_range(pol)['noise'] - - def get_swath_id_vectors(self, polarization, line, pixel): + def get_swath_id_vectors(self, pol, pixel=None): + if pixel is None: + pixel = self.noise_range(pol)['pixel'] swath_indices = [np.zeros(p.shape, int) for p in pixel] - swathBounds = self.swath_bounds[polarization] + swathBounds = self.swath_bounds[pol] for iswath, swath_name in enumerate(swathBounds): swathBound = swathBounds[swath_name] @@ -320,24 +318,28 @@ def get_swath_id_vectors(self, polarization, line, pixel): swathBound['lastRangeSample'], ) for fal, lal, frs, lrs in zipped: - valid1 = np.where((line >= fal) * (line <= lal))[0] + valid1 = np.where( + (self.noise_range(pol)['line'] >= fal) * + (self.noise_range(pol)['line'] <= lal) + )[0] for v1 in valid1: valid2 = (pixel[v1] >= frs) * (pixel[v1] <= lrs) swath_indices[v1][valid2] = iswath + 1 return swath_indices - def get_eap_rsl_vectors(self, polarization, line, pixel, min_size=3, rsl_power=3./2.): + def get_eap_rsl_vectors(self, pol, min_size=3, rsl_power=3./2.): """ Compute Antenna Pattern Gain APG=(1/EAP/RSL)**2 for each noise vectors """ + pixel = self.noise_range(pol)['pixel'] eap_vectors = [np.zeros(p.size) + np.nan for p in pixel] rsl_vectors = [np.zeros(p.size) + np.nan for p in pixel] - swath_indices = self.get_swath_id_vectors(polarization, line, pixel) + swath_indices = self.get_swath_id_vectors(pol) for swid in self.swath_ids: swath_name = f'{self.obsMode}{swid}' - eap_interpolator = self.get_eap_interpolator(swath_name, polarization) - ba_interpolator = self.get_boresight_angle_interpolator(polarization) - rsl_interpolator = self.get_range_spread_loss_interpolator(polarization, rsl_power=rsl_power) - for i, (l, p, swids) in enumerate(zip(line, pixel, swath_indices)): + eap_interpolator = self.get_eap_interpolator(swath_name, pol) + ba_interpolator = self.get_boresight_angle_interpolator(pol) + rsl_interpolator = self.get_range_spread_loss_interpolator(pol, rsl_power=rsl_power) + for i, (l, p, swids) in enumerate(zip(self.noise_range(pol)['line'], pixel, swath_indices)): gpi = np.where(swids == swid)[0] if gpi.size > min_size: ba = ba_interpolator(l, p[gpi]).flatten() @@ -368,12 +370,12 @@ def get_pg_product(self, pol, pg_name='pgProductAmplitude'): pg_swaths[swid] = pg_vec return pg_swaths - def get_tg_vectors(self, polarization, line, pixel, min_size=3): + def get_tg_vectors(self, pol, min_size=3): """ Compute Total Gain TG=Gtot*/(EAP/RSL)**2 for each noise vectors """ - eap_vectors, rsl_vectors = self.get_eap_rsl_vectors(polarization, line, pixel, min_size=min_size, rsl_power=2) + eap_vectors, rsl_vectors = self.get_eap_rsl_vectors(pol, min_size=min_size, rsl_power=2) tg_vectors = [(1 / eap / rsl) ** 2 for (eap,rsl) in zip(eap_vectors, rsl_vectors)] - swath_ids = self.get_swath_id_vectors(polarization, line, pixel) - pg_swaths = self.get_pg_product(polarization) + swath_ids = self.get_swath_id_vectors(pol) + pg_swaths = self.get_pg_product(pol) for i, gtot in enumerate(tg_vectors): for j in range(1,6): gpi = swath_ids[i] == j @@ -391,9 +393,9 @@ def get_tg_scales_offsets(self): scales.append(p['B'][1+i*2] * p['A_SCALE'] / p['Y_SCALE']) return scales, offsets - def get_swath_interpolator(self, polarization, swath_name, line, pixel, z): + def get_swath_interpolator(self, pol, swath_name, line, pixel, z): """ Prepare interpolators for one swath """ - swathBound = self.swath_bounds[polarization][swath_name] + swathBound = self.swath_bounds[pol][swath_name] swath_coords = ( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -442,7 +444,9 @@ def get_elevation_incidence_angle_vectors(self, ea_interpolator, ia_interpolator ia = [ia_interpolator(l, p).flatten() for (l, p) in zip(line, pixel)] return ea, ia - def get_calibration_vectors(self, pol, line, pixel, name='sigmaNought'): + def get_calibration_vectors(self, pol, name='sigmaNought'): + line = self.noise_range(pol)['line'] + pixel = self.noise_range(pol)['pixel'] swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] s0line = self.calibration[pol]['line'] s0pixel = self.calibration[pol]['pixel'] @@ -468,13 +472,13 @@ def calibrate_noise_vectors(self, noise, cal_s0, scall): """ Compute calibrated NESZ from input noise, sigma0 calibration and scalloping noise""" return [s * n / c**2 for n, c, s in zip(noise, cal_s0, scall)] - def get_eap_interpolator(self, subswathID, polarization): + def get_eap_interpolator(self, subswathID, pol): """ Prepare interpolator for Elevation Antenna Pattern. It computes EAP for input boresight angles """ - elevationAntennaPatternLUT = self.import_elevationAntennaPattern(polarization) + elevationAntennaPatternLUT = self.import_elevationAntennaPattern(pol) eap_lut = np.array(elevationAntennaPatternLUT[subswathID]['elevationAntennaPattern']) recordLength = elevationAntennaPatternLUT[subswathID]['elevationAntennaPatternCount'] eai_lut = elevationAntennaPatternLUT[subswathID]['elevationAngleIncrement'] @@ -524,19 +528,24 @@ def get_range_spread_loss_interpolator(self, pol, rsl_power=3./2.): self.geolocation[pol]['line'], self.geolocation[pol]['pixel'], rangeSpreadingLoss) return rsp_interpolator - def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip=4, min_valid_size=10, rsl_power=3./2.): + def get_shifted_noise_vectors(self, pol, pixel=None, noise=None, skip=4, min_valid_size=10, rsl_power=3./2.): """ Estimate shift in range noise LUT relative to antenna gain pattern and correct for it. """ + if pixel is None: + pixel = self.noise_range(pol)['pixel'] + if noise is None: + noise = self.noise_range(pol)['noise'] + line = self.noise_range(pol)['line'] noise_shifted = [np.zeros(p.size) for p in pixel] # noise lut shift for swid in self.swath_ids: swath_name = f'{self.obsMode}{swid}' - swathBound = self.swath_bounds[polarization][swath_name] - eap_interpolator = self.get_eap_interpolator(swath_name, polarization) - ba_interpolator = self.get_boresight_angle_interpolator(polarization) - rsp_interpolator = self.get_range_spread_loss_interpolator(polarization, rsl_power=rsl_power) + swathBound = self.swath_bounds[pol][swath_name] + eap_interpolator = self.get_eap_interpolator(swath_name, pol) + ba_interpolator = self.get_boresight_angle_interpolator(pol) + rsp_interpolator = self.get_range_spread_loss_interpolator(pol, rsl_power=rsl_power) zipped = zip( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -573,13 +582,16 @@ def get_shifted_noise_vectors(self, polarization, line, pixel, noise, skip=4, mi return noise_shifted - def get_corrected_noise_vectors(self, polarization, line, pixel, nesz, add_pb=True): + def get_corrected_noise_vectors(self, pol, nesz, pixel=None, add_pb=True): """ Load scaling and offset coefficients from files and apply to input NESZ """ + if pixel is None: + pixel = self.noise_range(pol)['pixel'] + line = self.noise_range(pol)['line'] nesz_corrected = [np.zeros(p.size)+np.nan for p in pixel] - ns, pb = self.import_denoisingCoefficients(polarization)[:2] + ns, pb = self.import_denoisingCoefficients(pol)[:2] for swid in self.swath_ids: swath_name = f'{self.obsMode}{swid}' - swathBound = self.swath_bounds[polarization][swath_name] + swathBound = self.swath_bounds[pol][swath_name] zipped = zip( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -595,14 +607,16 @@ def get_corrected_noise_vectors(self, polarization, line, pixel, nesz, add_pb=Tr nesz_corrected[v1][valid2] += pb[swath_name] return nesz_corrected - def get_raw_sigma0_vectors(self, polarization, line, pixel, cal_s0, average_lines=111): + def get_raw_sigma0_vectors(self, pol, cal_s0, average_lines=111): """ Read DN_ values from input GeoTIff for a given lines, average in azimuth direction, compute sigma0, and return sigma0 for given pixels """ + line = self.noise_range(pol)['line'] + pixel = self.noise_range(pol)['pixel'] ws2 = np.floor(average_lines / 2) raw_sigma0 = [np.zeros(p.size)+np.nan for p in pixel] - src_filename = self.bands()[self.get_band_number(f'DN_{polarization}')]['SourceFilename'] + src_filename = self.bands()[self.get_band_number(f'DN_{pol}')]['SourceFilename'] ds = gdal.Open(src_filename) img_data = ds.ReadAsArray().astype(float) img_data[img_data == 0] = np.nan @@ -663,13 +677,15 @@ def get_ia_vectors_from_full_size(self, line, pixel, ia_fs): ia_res[i] = ia_fs[line[i]][pixel[i]] return ia_res - def compute_rqm(self, s0, polarization, line, pixel, num_px=100, **kwargs): + def compute_rqm(self, s0, pol, num_px=100, **kwargs): """ Compute Range Quality Metric from the input sigma0 """ + line = self.noise_range(pol)['line'] + pixel = self.noise_range(pol)['pixel'] q_all = {} for swid in self.swath_ids[:-1]: q_subswath = [] swath_name = f'{self.obsMode}{swid}' - swathBound = self.swath_bounds[polarization][swath_name] + swathBound = self.swath_bounds[pol][swath_name] zipped = zip( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -692,28 +708,27 @@ def compute_rqm(self, s0, polarization, line, pixel, num_px=100, **kwargs): q_all[swath_name] = np.array(q_subswath) return q_all - def get_range_quality_metric(self, polarization='HV', **kwargs): + def get_range_quality_metric(self, pol='HV', **kwargs): """ Compute sigma0 with four methods (ESA, SHIFTED, NERSC, TG), compute RQM for each sigma0 """ - line, pixel, noise = self.get_noise_range_vectors(polarization) - cal_s0 = self.get_calibration_vectors(polarization, line, pixel) + cal_s0 = self.get_calibration_vectors(pol) if self.IPFversion < 2.9: - scall_esa = [np.ones(p.size) for p in pixel] + scall_esa = [np.ones(p.size) for p in self.noise_range(pol)['pixel']] else: - scall_esa = self.get_noise_azimuth_vectors(polarization, line, pixel) - nesz_esa = self.calibrate_noise_vectors(noise, cal_s0, scall_esa) - scall = self.get_noise_azimuth_vectors(polarization, line, pixel) - noise_shifted = self.get_shifted_noise_vectors(polarization, line, pixel, noise) + scall_esa = self.get_noise_azimuth_vectors(pol) + nesz_esa = self.calibrate_noise_vectors(self.noise_range(pol)['noise'], cal_s0, scall_esa) + scall = self.get_noise_azimuth_vectors(pol) + noise_shifted = self.get_shifted_noise_vectors(pol) nesz_shifted = self.calibrate_noise_vectors(noise_shifted, cal_s0, scall) - nesz_corrected = self.get_corrected_noise_vectors(polarization, line, pixel, nesz_shifted) - noise_tg = self.get_noise_tg_vectors(polarization, line, pixel) + nesz_corrected = self.get_corrected_noise_vectors(pol, nesz_shifted) + noise_tg = self.get_noise_tg_vectors(pol) nesz_tg = self.calibrate_noise_vectors(noise_tg, cal_s0, scall) - sigma0 = self.get_raw_sigma0_vectors(polarization, line, pixel, cal_s0) + sigma0 = self.get_raw_sigma0_vectors(pol, cal_s0) s0_esa = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_esa)] s0_shift = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_shifted)] s0_nersc = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_corrected)] s0_tg = [s0 - n0 for (s0,n0) in zip(sigma0, nesz_tg)] - q = [self.compute_rqm(s0, polarization, line, pixel) for s0 in [s0_esa, s0_shift, s0_nersc, s0_tg]] + q = [self.compute_rqm(s0, pol) for s0 in [s0_esa, s0_shift, s0_nersc, s0_tg]] alg_names = ['ESA', 'SHIFT', 'NERSC', 'TG'] var_names = ['RQM', 'AVG1', 'AVG2', 'STD1', 'STD2'] q_all = {'IPF': self.IPFversion} @@ -725,29 +740,30 @@ def get_range_quality_metric(self, polarization='HV', **kwargs): q_all[f'LINE_{swath_name}'] = list(q[alg_i][swath_name][:, 5]) return q_all - def experiment_get_data(self, polarization, average_lines, zoom_step): + def experiment_get_data(self, pol, average_lines, zoom_step): """ Prepare data for noiseScaling and powerBalancing experiments """ crop = {'IW':400, 'EW':200}[self.obsMode] - line, pixel0, noise0 = self.get_noise_range_vectors(polarization) - cal_s00 = self.get_calibration_vectors(polarization, line, pixel0) + pixel0 = self.noise_range['pixel'] + noise0 = self.noise_range['noise'] + cal_s00 = self.get_calibration_vectors(pol) # zoom: pixel = [np.arange(p[0], p[-1], zoom_step) for p in pixel0] noise = [interp1d(p, n)(p2) for (p,n,p2) in zip(pixel0, noise0, pixel)] cal_s0 = [interp1d(p, n)(p2) for (p,n,p2) in zip(pixel0, cal_s00, pixel)] - noise_shifted = self.get_shifted_noise_vectors(polarization, line, pixel, noise) - scall = self.get_noise_azimuth_vectors(polarization, line, pixel) + noise_shifted = self.get_shifted_noise_vectors(pol, pixel, noise) + scall = self.get_noise_azimuth_vectors(pol, pixel) nesz = self.calibrate_noise_vectors(noise_shifted, cal_s0, scall) - sigma0_fs = self.get_raw_sigma0_full_size(polarization) + sigma0_fs = self.get_raw_sigma0_full_size(pol) + line = self.noise_range(pol)['line'] sigma0 = self.get_raw_sigma0_vectors_from_full_size( - polarization, line, pixel, sigma0_fs, average_lines=average_lines) + pol, line, pixel, sigma0_fs, average_lines=average_lines) + return line, pixel, sigma0, nesz, crop, self.swath_bounds[pol] - return line, pixel, sigma0, nesz, crop, self.swath_bounds[polarization] - - def experiment_noiseScaling(self, polarization, average_lines=777, zoom_step=2): + def experiment_noiseScaling(self, pol, average_lines=777, zoom_step=2): """ Compute noise scaling coefficients for each range noise line and save as NPZ """ line, pixel, sigma0, nesz, crop, swathBounds = self.experiment_get_data( - polarization, average_lines, zoom_step) + pol, average_lines, zoom_step) results = {} results['IPFversion'] = self.IPFversion @@ -790,12 +806,10 @@ def experiment_noiseScaling(self, polarization, average_lines=777, zoom_step=2): results[swath_name]['fitResidual'].append(fitResidual) np.savez(self.filename.split('.')[0] + '_noiseScaling.npz', **results) - def experiment_powerBalancing(self, polarization, average_lines=777, zoom_step=2): + def experiment_powerBalancing(self, pol, average_lines=777, zoom_step=2): """ Compute power balancing coefficients for each range noise line and save as NPZ """ - line, pixel, sigma0, nesz, crop, swathBounds = self.experiment_get_data( - polarization, average_lines, zoom_step) - nesz_corrected = self.get_corrected_noise_vectors( - polarization, line, pixel, nesz, add_pb=False) + line, pixel, sigma0, nesz, crop, swathBounds = self.experiment_get_data(pol, average_lines, zoom_step) + nesz_corrected = self.get_corrected_noise_vectors(pol, nesz, pixel=pixel, add_pb=False) num_swaths = len(self.swath_ids) swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] @@ -899,13 +913,13 @@ def experiment_powerBalancing(self, polarization, average_lines=777, zoom_step=2 np.savez(self.filename.split('.')[0] + '_powerBalancing.npz', **results) - def interp_nrv_full_size(self, z, line, pixel, polarization, power=1): + def interp_nrv_full_size(self, z, line, pixel, pol, power=1): """ Interpolate noise range vectors to full size """ z_fs = np.zeros(self.shape()) + np.nan - swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] + swath_names = [f'{self.obsMode}{i}' for i in self.swath_ids] for swath_name in swath_names: z_interp2, swath_coords = self.get_swath_interpolator( - polarization, swath_name, line, pixel, z) + pol, swath_name, line, pixel, z) for fal, lal, frs, lrs in zip(*swath_coords): pix_vec_fr = np.arange(frs, lrs+1) lin_vec_fr = np.arange(fal, lal+1) @@ -915,16 +929,16 @@ def interp_nrv_full_size(self, z, line, pixel, polarization, power=1): z_fs[fal:lal+1, frs:lrs+1] = z_arr_fs return z_fs - def get_corrected_nesz_full_size(self, polarization, nesz): + def get_corrected_nesz_full_size(self, pol, nesz): """ Get corrected NESZ on full size matrix """ nesz_corrected = np.array(nesz) - ns, pb = self.import_denoisingCoefficients(polarization)[:2] + ns, pb = self.import_denoisingCoefficients(pol)[:2] for swid in self.swath_ids: swath_name = f'{self.obsMode}{swid}' # skip correction id NS/PB coeffs are not available (e.g. HH or VV) if swath_name not in ns: continue - swathBound = self.swath_bounds[polarization][swath_name] + swathBound = self.swath_bounds[pol][swath_name] zipped = zip( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -950,65 +964,69 @@ def get_raw_sigma0_full_size(self, pol, min_dn=0): sigma0_fs[dn <= min_dn] = np.nan return sigma0_fs - def export_noise_xml(self, polarization, output_path): + def export_noise_xml(self, pol, output_path): """ Export corrected (shifted and scaled) range noise into XML file """ crosspol_noise_file = [fn for fn in glob.glob(self.filename+'/annotation/calibration/*') - if 'noise-s1' in fn and '-%s-' % polarization.lower() in fn][0] + if 'noise-s1' in fn and '-%s-' % pol.lower() in fn][0] - line, pixel, noise0 = self.get_noise_range_vectors(polarization) - noise1 = self.get_shifted_noise_vectors(polarization, line, pixel, noise0) - noise2 = self.get_corrected_noise_vectors(polarization, line, pixel, noise1) + noise1 = self.get_shifted_noise_vectors(pol) + noise2 = self.get_corrected_noise_vectors(pol, noise1) tree = ET.parse(crosspol_noise_file) root = tree.getroot() - for noiseRangeVector, pixel_vec, noise_vec in zip(root.iter('noiseRangeVector'), pixel, noise2): + for noiseRangeVector, pixel_vec, noise_vec in zip(root.iter('noiseRangeVector'), self.noise_range(pol)['pixel'], noise2): noise_vec[np.isnan(noise_vec)] = 0 noiseRangeVector.find('pixel').text = ' '.join([f'{p}' for p in list(pixel_vec)]) noiseRangeVector.find('noiseRangeLut').text = ' '.join([f'{p}' for p in list(noise_vec)]) tree.write(os.path.join(output_path, os.path.basename(crosspol_noise_file))) return crosspol_noise_file - def get_noise_tg_vectors(self, polarization, line, pixel): + def get_noise_tg_vectors(self, pol): + pixel = self.noise_range(pol)['pixel'] noise = [np.zeros_like(i) for i in pixel] scales, offsets = self.get_tg_scales_offsets() - gtot = self.get_tg_vectors(polarization, line, pixel) - swath_ids = self.get_swath_id_vectors(polarization, line, pixel) + gtot = self.get_tg_vectors(pol) + swath_ids = self.get_swath_id_vectors(pol) for i in range(len(gtot)): for j in range(1,6): gpi = swath_ids[i] == j noise[i][gpi] = offsets[j-1] + gtot[i][gpi] * scales[j-1] return noise - def get_nesz_full_size(self, polarization, algorithm): + def get_nesz_full_size(self, pol, algorithm): # ESA noise vectors - line, pixel, noise = self.get_noise_range_vectors(polarization) + noise = self.noise_range(pol)['noise'] if algorithm == 'NERSC': # NERSC correction of noise shift in range direction - noise = self.get_shifted_noise_vectors(polarization, line, pixel, noise) + noise = self.get_shifted_noise_vectors(pol) elif algorithm == 'NERSC_TG': # Total Gain - based noise vectors - noise = self.get_noise_tg_vectors(polarization, line, pixel) + noise = self.get_noise_tg_vectors(pol) # noise calibration - cal_s0 = self.get_calibration_vectors(polarization, line, pixel) + cal_s0 = self.get_calibration_vectors(pol) nesz = [n / c**2 for (n,c) in zip(noise, cal_s0)] # noise full size - nesz_fs = self.interp_nrv_full_size(nesz, line, pixel, polarization) + nesz_fs = self.interp_nrv_full_size( + nesz, + self.noise_range(pol)['line'], + self.noise_range(pol)['pixel'], + pol) # scalloping correction - nesz_fs *= self.get_scalloping_full_size(polarization) + nesz_fs *= self.get_scalloping_full_size(pol) # NERSC correction of NESZ magnitude if algorithm == 'NERSC': - nesz_fs = self.get_corrected_nesz_full_size(polarization, nesz_fs) + nesz_fs = self.get_corrected_nesz_full_size(pol, nesz_fs) return nesz_fs - def remove_thermal_noise(self, polarization, algorithm='NERSC', remove_negative=True, min_dn=0): - nesz_fs = self.get_nesz_full_size(polarization, algorithm) - sigma0 = self.get_raw_sigma0_full_size(polarization, min_dn=min_dn) + def remove_thermal_noise(self, pol, algorithm='NERSC', remove_negative=True, min_dn=0): + nesz_fs = self.get_nesz_full_size(pol, algorithm) + sigma0 = self.get_raw_sigma0_full_size(pol, min_dn=min_dn) sigma0 -= nesz_fs if remove_negative: sigma0 = fill_gaps(sigma0, sigma0 <= 0) return sigma0 - def import_elevationAntennaPattern(self, polarization): + def import_elevationAntennaPattern(self, pol): ''' Import elevation antenna pattern from auxiliary calibration XML DOM ''' calParamsList = self.auxiliaryCalibrationXML.getElementsByTagName('calibrationParams') elevationAntennaPattern = { '%s%s' % (self.obsMode, li): @@ -1022,7 +1040,7 @@ def import_elevationAntennaPattern(self, polarization): for iList in calParamsList: swath = get_DOM_nodeValue(iList,['swath']) pol = get_DOM_nodeValue(iList,['polarisation']) - if (swath in elevationAntennaPattern.keys()) and (pol==polarization): + if (swath in elevationAntennaPattern.keys()) and (pol==pol): elem = iList.getElementsByTagName('elevationAntennaPattern')[0] for k in elevationAntennaPattern[swath].keys(): if k=='elevationAngleIncrement': @@ -1034,10 +1052,10 @@ def import_elevationAntennaPattern(self, polarization): elevationAntennaPattern[swath][k] = get_DOM_nodeValue(iList,[k],'float') return elevationAntennaPattern - def import_antennaPattern(self, polarization): + def import_antennaPattern(self, pol): ''' Import antenna pattern from annotation XML DOM ''' swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] - antennaPatternList = self.annotationXML[polarization].getElementsByTagName('antennaPattern') + antennaPatternList = self.annotationXML[pol].getElementsByTagName('antennaPattern') antennaPatternList = antennaPatternList[1:] keys = ['azimuthTime', 'slantRangeTime', 'elevationAngle', 'elevationPattern', 'incidenceAngle', 'terrainHeight', 'roll'] antennaPattern = {swath_name: {key: [] for key in keys} for swath_name in swath_names} @@ -1055,12 +1073,12 @@ def import_antennaPattern(self, polarization): antennaPattern[swath][k].append(get_DOM_nodeValue(iList,[k],'float')) if compute_roll: for swath_name in swath_names: - antennaPattern[swath_name]['roll'] = self.compute_roll(polarization, antennaPattern[swath_name]) + antennaPattern[swath_name]['roll'] = self.compute_roll(pol, antennaPattern[swath_name]) return antennaPattern - def import_orbit(self, polarization): + def import_orbit(self, pol): ''' Import orbit information from annotation XML DOM ''' - orbitList = self.annotationXML[polarization].getElementsByTagName('orbit') + orbitList = self.annotationXML[pol].getElementsByTagName('orbit') orbit = { 'time':[], 'position':{'x':[], 'y':[], 'z':[]}, 'velocity':{'x':[], 'y':[], 'z':[]} } @@ -1075,9 +1093,9 @@ def import_orbit(self, polarization): get_DOM_nodeValue(iList,['velocity',k],'float')) return orbit - def orbitAtGivenTime(self, polarization, relativeAzimuthTime): + def orbitAtGivenTime(self, pol, relativeAzimuthTime): ''' Interpolate orbit parameters for given time vector ''' - stateVectors = self.import_orbit(polarization) + stateVectors = self.import_orbit(pol) orbitTime = np.array([ (t-self.time_coverage_center).total_seconds() for t in stateVectors['time'] ]) orbitAtGivenTime = { 'relativeAzimuthTime':relativeAzimuthTime, @@ -1094,12 +1112,12 @@ def orbitAtGivenTime(self, polarization, relativeAzimuthTime): orbitAtGivenTime[k] = np.squeeze(orbitAtGivenTime[k]) return orbitAtGivenTime - def compute_roll(self, polarization, antenna_pattern): + def compute_roll(self, pol, antenna_pattern): relativeAzimuthTime = np.array([ (t - self.time_coverage_center).total_seconds() for t in antenna_pattern['azimuthTime'] ]) - positionXYZ = self.orbitAtGivenTime(polarization, relativeAzimuthTime)['positionXYZ'] + positionXYZ = self.orbitAtGivenTime(pol, relativeAzimuthTime)['positionXYZ'] satelliteLatitude = np.arctan2(positionXYZ[:,2], np.sqrt(positionXYZ[:,0]**2 + positionXYZ[:,1]**2)) r_major = 6378137.0 # WGS84 semi-major axis r_minor = 6356752.314245179 # WGS84 semi-minor axis @@ -1120,7 +1138,7 @@ def load_denoising_parameters_json(self): params = json.load(f) return params - def import_denoisingCoefficients(self, polarization, load_extra_scaling=False): + def import_denoisingCoefficients(self, pol, load_extra_scaling=False): ''' Import denoising coefficients ''' filename_parts = os.path.basename(self.filename).split('_') platform = filename_parts[0] @@ -1140,7 +1158,7 @@ def import_denoisingCoefficients(self, polarization, load_extra_scaling=False): # After this change, the scaling parameters seems be much closer to those of IPFv 2.8. IPFversion = 2.8 - base_key = f'{platform}_{mode}_{resolution}_{polarization}' + base_key = f'{platform}_{mode}_{resolution}_{pol}' for iSW in self.swath_ids: subswathID = '%s%s' % (self.obsMode, iSW) @@ -1178,7 +1196,7 @@ def import_denoisingCoefficients(self, polarization, load_extra_scaling=False): return ( noiseScalingParameters, powerBalancingParameters, extraScalingParameters, noiseVarianceParameters ) - def remove_texture_noise(self, polarization, window=3, weight=0.1, s0_min=0, remove_negative=True, algorithm='NERSC', min_dn=0, **kwargs): + def remove_texture_noise(self, pol, window=3, weight=0.1, s0_min=0, remove_negative=True, algorithm='NERSC', min_dn=0, **kwargs): """ Thermal noise removal followed by textural noise compensation using Method2 Method2 is implemented as a weighted average of sigma0 and sigma0 smoothed with @@ -1188,7 +1206,7 @@ def remove_texture_noise(self, polarization, window=3, weight=0.1, s0_min=0, rem Parameters ---------- - polarization : str + pol : str 'HH' or 'HV' window : int Size of window in the gaussian filter @@ -1206,8 +1224,8 @@ def remove_texture_noise(self, polarization, window=3, weight=0.1, s0_min=0, rem if self.IPFversion == 3.2: self.IPFversion = 3.1 - sigma0 = self.get_raw_sigma0_full_size(polarization, min_dn=min_dn) - nesz = self.get_nesz_full_size(polarization, algorithm) + sigma0 = self.get_raw_sigma0_full_size(pol, min_dn=min_dn) + nesz = self.get_nesz_full_size(pol, algorithm) s0_offset = np.nanmean(nesz) if s0_offset == 0: sigma0o = sigma0 @@ -1222,11 +1240,11 @@ def remove_texture_noise(self, polarization, window=3, weight=0.1, s0_min=0, rem return sigma0o - def subswathIndexMap(self, polarization): + def subswathIndexMap(self, pol): ''' Convert subswath indices into full grid pixels ''' subswathIndexMap = np.zeros(self.shape(), dtype=np.uint8) for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): - swathBound = self.swath_bounds[polarization]['%s%s' % (self.obsMode, iSW)] + swathBound = self.swath_bounds[pol]['%s%s' % (self.obsMode, iSW)] zipped = zip(swathBound['firstAzimuthLine'], swathBound['firstRangeSample'], swathBound['lastAzimuthLine'], @@ -1235,7 +1253,7 @@ def subswathIndexMap(self, polarization): subswathIndexMap[fal:lal+1,frs:lrs+1] = iSW return subswathIndexMap - def import_azimuthAntennaElementPattern(self, polarization): + def import_azimuthAntennaElementPattern(self, pol): ''' Import azimuth antenna element pattern from auxiliary calibration XML DOM ''' calParamsList = self.auxiliaryCalibrationXML.getElementsByTagName('calibrationParams') azimuthAntennaElementPattern = { '%s%s' % (self.obsMode, li): @@ -1247,7 +1265,7 @@ def import_azimuthAntennaElementPattern(self, polarization): for iList in calParamsList: swath = get_DOM_nodeValue(iList,['swath']) pol = get_DOM_nodeValue(iList,['polarisation']) - if (swath in azimuthAntennaElementPattern.keys()) and (pol==polarization): + if (swath in azimuthAntennaElementPattern.keys()) and (pol==pol): elem = iList.getElementsByTagName('azimuthAntennaElementPattern')[0] for k in azimuthAntennaElementPattern[swath].keys(): if k=='azimuthAngleIncrement': @@ -1261,9 +1279,9 @@ def import_azimuthAntennaElementPattern(self, polarization): get_DOM_nodeValue(iList,[k],'float') ) return azimuthAntennaElementPattern - def subswathCenterSampleIndex(self, polarization): + def subswathCenterSampleIndex(self, pol): ''' Range center pixel indices along azimuth for each subswath ''' - swathBounds = self.swath_bounds[polarization] + swathBounds = self.swath_bounds[pol] subswathCenterSampleIndex = {} for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): subswathID = '%s%s' % (self.obsMode, iSW) @@ -1285,7 +1303,7 @@ def geolocationGridPointInterpolator(self, pol, itemName): self.geolocation[pol]['line'], self.geolocation[pol]['pixel'], z) return interpolator - def azimuthFmRateAtGivenTime(self, polarization, relativeAzimuthTime, slantRangeTime): + def azimuthFmRateAtGivenTime(self, pol, relativeAzimuthTime, slantRangeTime): ''' Get azimuth frequency modulation rate for given time vectors Returns @@ -1294,7 +1312,7 @@ def azimuthFmRateAtGivenTime(self, polarization, relativeAzimuthTime, slantRange ''' if relativeAzimuthTime.size != slantRangeTime.size: raise ValueError('relativeAzimuthTime and slantRangeTime must have the same dimension') - azimuthFmRate = self.import_azimuthFmRate(polarization) + azimuthFmRate = self.import_azimuthFmRate(pol) azimuthFmRatePolynomial = np.array(azimuthFmRate['azimuthFmRatePolynomial']) t0 = np.array(azimuthFmRate['t0']) xp = np.array([ (t-self.time_coverage_center).total_seconds() @@ -1307,9 +1325,9 @@ def azimuthFmRateAtGivenTime(self, polarization, relativeAzimuthTime, slantRange azimuthFmRateAtGivenTime.append(np.interp(tt[0], xp, fp)) return np.squeeze(azimuthFmRateAtGivenTime) - def import_azimuthFmRate(self, polarization): + def import_azimuthFmRate(self, pol): ''' Import azimuth frequency modulation rate from annotation XML DOM ''' - soup = BeautifulSoup(self.annotationXML[polarization].toxml(), features="xml") + soup = BeautifulSoup(self.annotationXML[pol].toxml(), features="xml") azimuthFmRate = defaultdict(list) for afmr in soup.find_all('azimuthFmRate'): azimuthFmRate['azimuthTime'].append(datetime.strptime(afmr.azimuthTime.text, '%Y-%m-%dT%H:%M:%S.%f')) @@ -1321,7 +1339,7 @@ def import_azimuthFmRate(self, polarization): azimuthFmRate['azimuthFmRatePolynomial'].append(afmrp) return azimuthFmRate - def focusedBurstLengthInTime(self, polarization): + def focusedBurstLengthInTime(self, pol): ''' Get focused burst length in zero-Doppler time domain Returns @@ -1330,9 +1348,9 @@ def focusedBurstLengthInTime(self, polarization): one values for each subswath (different for IW and EW) ''' azimuthFrequency = get_DOM_nodeValue( - self.annotationXML[polarization],['azimuthFrequency'],'float') + self.annotationXML[pol],['azimuthFrequency'],'float') azimuthTimeIntevalInSLC = 1. / azimuthFrequency - inputDimensionsList = self.annotationXML[polarization].getElementsByTagName( + inputDimensionsList = self.annotationXML[pol].getElementsByTagName( 'inputDimensions') focusedBurstLengthInTime = {} # nominalLinesPerBurst should be smaller than the real values @@ -1350,36 +1368,36 @@ def focusedBurstLengthInTime(self, polarization): raise ValueError('number of bursts cannot be determined.') return focusedBurstLengthInTime - def get_subswathScallopingGain(self, polarization, subswathID): + def get_subswathScallopingGain(self, pol, subswathID): # azimuth antenna element patterns (AAEP) lookup table for given subswath - AAEP = self.import_azimuthAntennaElementPattern(polarization)[subswathID] + AAEP = self.import_azimuthAntennaElementPattern(pol)[subswathID] gainAAEP = np.array(AAEP['azimuthAntennaElementPattern']) angleAAEP = ( np.arange(-(len(gainAAEP)//2), len(gainAAEP)//2+1) * AAEP['azimuthAngleIncrement'] ) # subswath range center pixel index - subswathCenterSampleIndex = self.subswathCenterSampleIndex(polarization)[subswathID] + subswathCenterSampleIndex = self.subswathCenterSampleIndex(pol)[subswathID] # slant range time along subswath range center - interpolator = self.geolocationGridPointInterpolator(polarization, 'slantRangeTime') + interpolator = self.geolocationGridPointInterpolator(pol, 'slantRangeTime') slantRangeTime = np.squeeze(interpolator(np.arange(self.shape()[0]), subswathCenterSampleIndex)) # relative azimuth time along subswath range center - interpolator = self.geolocationGridPointInterpolator(polarization, 'azimuthTime') + interpolator = self.geolocationGridPointInterpolator(pol, 'azimuthTime') azimuthTime = np.squeeze(interpolator(np.arange(self.shape()[0]), subswathCenterSampleIndex)) # Doppler rate induced by satellite motion - motionDopplerRate = self.azimuthFmRateAtGivenTime(polarization, azimuthTime, slantRangeTime) + motionDopplerRate = self.azimuthFmRateAtGivenTime(pol, azimuthTime, slantRangeTime) # antenna steering rate antennaSteeringRate = np.deg2rad(ANTENNA_STEERING_RATE[subswathID]) # satellite absolute velocity along subswath range center - satelliteVelocity = np.linalg.norm(self.orbitAtGivenTime(polarization, azimuthTime)['velocityXYZ'], axis=1) + satelliteVelocity = np.linalg.norm(self.orbitAtGivenTime(pol, azimuthTime)['velocityXYZ'], axis=1) # Doppler rate induced by TOPS steering of antenna steeringDopplerRate = 2 * satelliteVelocity / RADAR_WAVELENGTH * antennaSteeringRate # combined Doppler rate (net effect) combinedDopplerRate = motionDopplerRate * steeringDopplerRate / (motionDopplerRate - steeringDopplerRate) # full burst length in zero-Doppler time - fullBurstLength = self.focusedBurstLengthInTime(polarization)[subswathID] + fullBurstLength = self.focusedBurstLengthInTime(pol)[subswathID] # zero-Doppler azimuth time at each burst start burstStartTime = np.array([ (t-self.time_coverage_center).total_seconds() - for t in self.import_antennaPattern(polarization)[subswathID]['azimuthTime'] ]) + for t in self.import_antennaPattern(pol)[subswathID]['azimuthTime'] ]) # burst overlapping length burstOverlap = fullBurstLength - np.diff(burstStartTime) burstOverlap = np.hstack([burstOverlap[0], burstOverlap]) @@ -1408,11 +1426,14 @@ def get_subswathScallopingGain(self, polarization, subswathID): scallopingGain = 1. / 10**(burstAAEP/10.) return scallopingGain - def get_noise_azimuth_vectors(self, pol, line, pixel): - """ Interpolate scalloping noise from XML files to input line/pixel coords """ + def get_noise_azimuth_vectors(self, pol, pixel=None): + """ Interpolate scalloping noise from XML files to range noise line/pixel coords """ + if pixel is None: + pixel = self.noise_range(pol)['pixel'] + line = self.noise_range(pol)['line'] scall = [np.zeros(p.size) for p in pixel] if self.IPFversion < 2.9: - swath_idvs = self.get_swath_id_vectors(pol, line, pixel) + swath_idvs = self.get_swath_id_vectors(pol, pixel) for i, l in enumerate(line): for j in range(1,6): gpi = swath_idvs[i] == j From 7c4e831874c13ee3f511ac0cb18cbe5b561dbae6 Mon Sep 17 00:00:00 2001 From: akorosov Date: Mon, 11 Dec 2023 10:07:58 +0100 Subject: [PATCH 17/25] refactor self.annotationXML --- s1denoise/sentinel1image.py | 97 ++++++++++++++++--------------------- 1 file changed, 42 insertions(+), 55 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index 89340d6..afd1c16 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -73,9 +73,9 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): noiseFiles = [fn for fn in zf.namelist() if 'annotation/calibration/noise-s1' in fn] for pol in [txPol + 'H', txPol + 'V']: - # TODO: replace self.annotationXML[pol].toxml() with raw XML - self.annotationXML[pol] = parseString( - [zf.read(fn) for fn in annotationFiles if pol.lower() in fn][0]) + self.annotationXML[pol] = BeautifulSoup( + [zf.read(fn) for fn in annotationFiles if pol.lower() in fn][0], + features="xml") self.calibrationXML[pol] = parseString( [zf.read(fn) for fn in calibrationFiles if pol.lower() in fn][0]) self.noiseXML[pol] = BeautifulSoup( @@ -94,7 +94,7 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): for fn in annotationFiles: if pol.lower() in fn: with open(fn) as ff: - self.annotationXML[pol] = parseString(ff.read()) + self.annotationXML[pol] = BeautifulSoup(ff.read(), features="xml") for fn in calibrationFiles: if pol.lower() in fn: @@ -153,9 +153,8 @@ def swath_bounds(self): swath_bounds = {} for pol in self.pols: - soup = BeautifulSoup(self.annotationXML[pol].toxml(), features="xml") swath_bounds[pol] = {} - for swathMerge in soup.find_all('swathMerge'): + for swathMerge in self.annotationXML[pol].find_all('swathMerge'): swath_bounds[pol][swathMerge.swath.text] = defaultdict(list) for swathBounds in swathMerge.find_all('swathBounds'): for name in names: @@ -178,9 +177,8 @@ def geolocation(self): } geolocation = {} for pol in self.pols: - soup = BeautifulSoup(self.annotationXML[pol].toxml(), features="xml") geolocation[pol] = defaultdict(list) - for p in soup.find_all('geolocationGridPoint'): + for p in self.annotationXML[pol].find_all('geolocationGridPoint'): for c in p: if c.name: geolocation[pol][c.name].append(geolocation_keys[c.name](c.text)) @@ -353,9 +351,8 @@ def get_eap_rsl_vectors(self, pol, min_size=3, rsl_power=3./2.): def get_pg_product(self, pol, pg_name='pgProductAmplitude'): azimuth_time = [(t - self.time_coverage_center).total_seconds() for t in self.noise_range(pol)['azimuthTime']] - annotation_soup = BeautifulSoup(self.annotationXML[pol].toxml(), features="xml") pg = defaultdict(dict) - for pgpa in annotation_soup.find_all(pg_name): + for pgpa in self.annotationXML[pol].find_all(pg_name): pg[pgpa.parent.parent.parent.swath.text][pgpa.parent.azimuthTime.text] = float(pgpa.text) pg_swaths = {} @@ -494,7 +491,7 @@ def get_eap_interpolator(self, subswathID, pol): def get_boresight_angle_interpolator(self, pol): """ Prepare interpolator for boresight angles. It computes BA for input x,y coordinates. """ - antennaPattern = self.import_antennaPattern(pol) + antennaPattern = self.antenna_pattern(pol) relativeAzimuthTime = [] for iSW in self.swath_ids: subswathID = '%s%s' % (self.obsMode, iSW) @@ -521,8 +518,7 @@ def get_range_spread_loss_interpolator(self, pol, rsl_power=3./2.): rsl_power : float power for RSL = (2 * Rref / time / C)^rsl_power """ - referenceRange = float(self.annotationXML[pol].getElementsByTagName( - 'referenceRange')[0].childNodes[0].nodeValue) + referenceRange = float(self.annotationXML[pol].find('referenceRange').text) rangeSpreadingLoss = (referenceRange / self.geolocation[pol]['slantRangeTime'] / SPEED_OF_LIGHT * 2)**rsl_power rsp_interpolator = RectBivariateSpline( self.geolocation[pol]['line'], self.geolocation[pol]['pixel'], rangeSpreadingLoss) @@ -1052,45 +1048,39 @@ def import_elevationAntennaPattern(self, pol): elevationAntennaPattern[swath][k] = get_DOM_nodeValue(iList,[k],'float') return elevationAntennaPattern - def import_antennaPattern(self, pol): - ''' Import antenna pattern from annotation XML DOM ''' - swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] - antennaPatternList = self.annotationXML[pol].getElementsByTagName('antennaPattern') - antennaPatternList = antennaPatternList[1:] - keys = ['azimuthTime', 'slantRangeTime', 'elevationAngle', 'elevationPattern', 'incidenceAngle', 'terrainHeight', 'roll'] - antennaPattern = {swath_name: {key: [] for key in keys} for swath_name in swath_names} - compute_roll = False - for iList in antennaPatternList: - swath = get_DOM_nodeValue(iList, ['swath']) - for k in antennaPattern[swath].keys(): - if k=='azimuthTime': - antennaPattern[swath][k].append( - datetime.strptime(get_DOM_nodeValue(iList,[k]),'%Y-%m-%dT%H:%M:%S.%f')) - elif k == 'roll' and len(iList.getElementsByTagName('roll')) == 0: - antennaPattern[swath][k].append(None) - compute_roll = True - else: - antennaPattern[swath][k].append(get_DOM_nodeValue(iList,[k],'float')) + @cache + def antenna_pattern(self, pol): + list_keys = ['slantRangeTime', 'elevationAngle', 'elevationPattern', 'incidenceAngle'] + antenna_pattern = {} + antennaPatternList = self.annotationXML[pol].find('antennaPatternList') + compute_roll = True + for antennaPattern in antennaPatternList.find_all('antennaPattern'): + swath = antennaPattern.swath.text + if swath not in antenna_pattern: + antenna_pattern[swath] = defaultdict(list) + antenna_pattern[swath]['azimuthTime'].append(parse_azimuth_time(antennaPattern.azimuthTime.text)) + for list_key in list_keys: + antenna_pattern[swath][list_key].append(np.array([float(i) for i in antennaPattern.find(list_key).text.split()])) + antenna_pattern[swath]['terrainHeight'].append(float(antennaPattern.terrainHeight.text)) + if antennaPattern.find('roll'): + antenna_pattern[swath]['roll'].append(float(antennaPattern.roll.text)) + compute_roll = False if compute_roll: - for swath_name in swath_names: - antennaPattern[swath_name]['roll'] = self.compute_roll(pol, antennaPattern[swath_name]) - return antennaPattern + for swath in antenna_pattern: + antenna_pattern[swath]['roll'] = self.compute_roll(pol, antenna_pattern[swath]) + return antenna_pattern + @cache def import_orbit(self, pol): ''' Import orbit information from annotation XML DOM ''' - orbitList = self.annotationXML[pol].getElementsByTagName('orbit') orbit = { 'time':[], 'position':{'x':[], 'y':[], 'z':[]}, 'velocity':{'x':[], 'y':[], 'z':[]} } - for iList in orbitList: - orbit['time'].append( - datetime.strptime(get_DOM_nodeValue(iList,['time']), '%Y-%m-%dT%H:%M:%S.%f')) - for k in orbit['position'].keys(): - orbit['position'][k].append( - get_DOM_nodeValue(iList,['position',k],'float')) - for k in orbit['velocity'].keys(): - orbit['velocity'][k].append( - get_DOM_nodeValue(iList,['velocity',k],'float')) + for o in self.annotationXML[pol].find('orbitList').find_all('orbit'): + orbit['time'].append(parse_azimuth_time(o.time.text)) + for name1 in ['position', 'velocity']: + for name2 in ['x', 'y', 'z']: + orbit[name1][name2].append(float(o.find(name1).find(name2).text)) return orbit def orbitAtGivenTime(self, pol, relativeAzimuthTime): @@ -1327,9 +1317,8 @@ def azimuthFmRateAtGivenTime(self, pol, relativeAzimuthTime, slantRangeTime): def import_azimuthFmRate(self, pol): ''' Import azimuth frequency modulation rate from annotation XML DOM ''' - soup = BeautifulSoup(self.annotationXML[pol].toxml(), features="xml") azimuthFmRate = defaultdict(list) - for afmr in soup.find_all('azimuthFmRate'): + for afmr in self.annotationXML[pol].find_all('azimuthFmRate'): azimuthFmRate['azimuthTime'].append(datetime.strptime(afmr.azimuthTime.text, '%Y-%m-%dT%H:%M:%S.%f')) azimuthFmRate['t0'].append(float(afmr.t0.text)) if 'azimuthFmRatePolynomial' in afmr.decode(): @@ -1339,6 +1328,7 @@ def import_azimuthFmRate(self, pol): azimuthFmRate['azimuthFmRatePolynomial'].append(afmrp) return azimuthFmRate + @cache def focusedBurstLengthInTime(self, pol): ''' Get focused burst length in zero-Doppler time domain @@ -1347,17 +1337,14 @@ def focusedBurstLengthInTime(self, pol): focusedBurstLengthInTime : dict one values for each subswath (different for IW and EW) ''' - azimuthFrequency = get_DOM_nodeValue( - self.annotationXML[pol],['azimuthFrequency'],'float') + azimuthFrequency = float(self.annotationXML[pol].find('azimuthFrequency').text) azimuthTimeIntevalInSLC = 1. / azimuthFrequency - inputDimensionsList = self.annotationXML[pol].getElementsByTagName( - 'inputDimensions') focusedBurstLengthInTime = {} # nominalLinesPerBurst should be smaller than the real values nominalLinesPerBurst = {'IW':1450, 'EW':1100}[self.obsMode] - for iList in inputDimensionsList: - swath = get_DOM_nodeValue(iList,['swath'],'str') - numberOfInputLines = get_DOM_nodeValue(iList,['numberOfInputLines'],'int') + for inputDimensions in self.annotationXML[pol].find_all('inputDimensions'): + swath = inputDimensions.swath.text + numberOfInputLines = int(inputDimensions.numberOfInputLines.text) numberOfBursts = max( [ primeNumber for primeNumber in range(1,numberOfInputLines//nominalLinesPerBurst+1) if (numberOfInputLines % primeNumber)==0 ] ) @@ -1397,7 +1384,7 @@ def get_subswathScallopingGain(self, pol, subswathID): # zero-Doppler azimuth time at each burst start burstStartTime = np.array([ (t-self.time_coverage_center).total_seconds() - for t in self.import_antennaPattern(pol)[subswathID]['azimuthTime'] ]) + for t in self.antenna_pattern(pol)[subswathID]['azimuthTime'] ]) # burst overlapping length burstOverlap = fullBurstLength - np.diff(burstStartTime) burstOverlap = np.hstack([burstOverlap[0], burstOverlap]) From bba85c2aa2308308ad6347f139ca67b37b40e137 Mon Sep 17 00:00:00 2001 From: akorosov Date: Mon, 11 Dec 2023 15:49:31 +0100 Subject: [PATCH 18/25] refactor Sentinel1ImageXml --- s1denoise/sentinel1image.py | 323 ++++++++++++++++-------------------- s1denoise/utils.py | 21 --- 2 files changed, 143 insertions(+), 201 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index afd1c16..baba018 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -3,10 +3,10 @@ import glob import json import os +from pathlib import Path import requests import shutil import xml.etree.ElementTree as ET -from xml.dom.minidom import parse, parseString import zipfile from functools import cached_property, cache @@ -22,7 +22,6 @@ from s1denoise.utils import ( cost, fit_noise_scaling_coeff, - get_DOM_nodeValue, fill_gaps, cubic_hermite_interpolation, parse_azimuth_time, @@ -40,6 +39,107 @@ 'EW4': 2.512694636, 'EW5': 2.122855427 } # degrees per second. Available from AUX_INS +class Sentinel1ImageXml: + def __init__(self, filename): + self.txPol = filename.split(os.sep)[-1][15] # H or V + self.platform = filename.split(os.sep)[-1][:3] # S1A or S1B + + # get list of all filenames + if zipfile.is_zipfile(filename): + with zipfile.PyZipFile(filename) as zf: + filenames = zf.namelist() + else: + filenames = [str(i) for i in Path(f'{filename}/').rglob('*')] + manifest_file = [f for f in filenames if 'manifest.safe' in f][0] + + # find annotation, calibration and noise files + self.xml = dict(annotation = {}, calibration = {}, noise = {}) + for pol in [self.txPol + 'H', self.txPol + 'V']: + self.xml['annotation'][pol] = [f for f in filenames if ('annotation/s1' in f and pol.lower() in f)][0] + self.xml['calibration'][pol] = [f for f in filenames if ('calibration-s1' in f and pol.lower() in f)][0] + self.xml['noise'][pol] = [f for f in filenames if ('noise-s1' in f and pol.lower() in f)][0] + + # read and parse XML files + if zipfile.is_zipfile(filename): + with zipfile.PyZipFile(filename) as zf: + for name in self.xml: + for pol in self.xml[name]: + self.xml[name][pol] = BeautifulSoup(zf.read(self.xml[name][pol]), features="xml") + self.xml['manifest'] = BeautifulSoup(zf.read(manifest_file), features="xml") + else: + for name in self.xml: + for pol in self.xml[name]: + with open(self.xml[name][pol]) as ff: + self.xml[name][pol] = BeautifulSoup(ff.read(), features="xml") + with open(manifest_file) as ff: + self.xml['manifest'] = BeautifulSoup(ff.read(), features="xml") + + # get the auxiliary calibration file + for resource in (self.xml['manifest'].find_all('resource') + + self.xml['manifest'].find_all('safe:resource')): + if resource.attrs['role'] == 'AUX_CAL': + auxCalibFilename = resource.attrs['name'].split('/')[-1] + self.set_aux_data_dir() + self.download_aux_calibration(auxCalibFilename) + with open(self.auxiliaryCalibration_file) as f: + self.xml['auxiliary'] = BeautifulSoup(f.read(), features="xml") + + def set_aux_data_dir(self): + """ Set directory where aux calibration data is stored """ + self.aux_data_dir = os.path.join(os.environ.get('XDG_DATA_HOME', os.path.expanduser('~')), + '.s1denoise') + if not os.path.exists(self.aux_data_dir): + os.makedirs(self.aux_data_dir) + + def download_aux_calibration(self, filename): + self.auxiliaryCalibration_file = os.path.join(self.aux_data_dir, filename, 'data', '%s-aux-cal.xml' % self.platform.lower()) + if os.path.exists(self.auxiliaryCalibration_file): + return + vs = filename.split('_')[3].lstrip('V') + validity_start = f'{vs[:4]}-{vs[4:6]}-{vs[6:8]}T{vs[9:11]}:{vs[11:13]}:{vs[13:15]}' + cd = filename.split('_')[4].lstrip('G') + creation_date = f'{cd[:4]}-{cd[4:6]}-{cd[6:8]}T{cd[9:11]}:{cd[11:13]}:{cd[13:15]}' + def get_remote_url(api_url): + with requests.get(api_url, stream=True) as r: + rjson = json.loads(r.content.decode()) + remote_url = rjson['results'][0]['remote_url'] + physical_name = rjson['results'][0]['physical_name'] + return remote_url, physical_name + try: + remote_url, physical_name = get_remote_url(f'https://sar-mpc.eu/api/v1/?product_type=AUX_CAL&validity_start={validity_start}&creation_date={creation_date}') + except: + remote_url, physical_name = get_remote_url(f'https://sar-mpc.eu/api/v1/?product_type=AUX_CAL&validity_start={validity_start}') + + download_file = os.path.join(self.aux_data_dir, physical_name) + print(f'downloading {filename}.zip from {remote_url}') + with requests.get(remote_url, stream=True) as r: + with open(download_file, "wb") as f: + f.write(r.content) + + with zipfile.ZipFile(download_file, 'r') as download_zip: + download_zip.extractall(path=self.aux_data_dir) + output_dir = f'{self.aux_data_dir}/{filename}' + if not os.path.exists(output_dir): + # in case the physical_name of the downloaded file does not exactly match the filename from manifest + # the best found AUX-CAL file is copied to the filename from manifest + shutil.copytree(download_file.rstrip('.zip'), output_dir) + + @property + def annotation(self): + return self.xml['annotation'] + @property + def calibration(self): + return self.xml['calibration'] + @property + def noise(self): + return self.xml['noise'] + @property + def manifest(self): + return self.xml['manifest'] + @property + def auxiliary(self): + return self.xml['auxiliary'] + class Sentinel1Image(Nansat): """ Cal/Val routines for Sentinel-1 performed on range noise vector coordinatess""" @@ -54,67 +154,17 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): 'EW_GRDM_1SDV' ] ): raise ValueError( 'Source file must be Sentinel-1A/1B ' 'IW_GRDH_1SDH, IW_GRDH_1SDV, EW_GRDM_1SDH, or EW_GRDM_1SDV product.' ) - self.platform = self.filename.split(os.sep)[-1][:3] # S1A or S1B self.obsMode = self.filename.split(os.sep)[-1][4:6] # IW or EW pol_mode = os.path.basename(self.filename).split('_')[3] self.crosspol = {'1SDH': 'HV', '1SDV': 'VH'}[pol_mode] self.pols = {'1SDH': ['HH', 'HV'], '1SDV': ['VH', 'VV']}[pol_mode] self.swath_ids = range(1, {'IW':3, 'EW':5}[self.obsMode]+1) - txPol = self.filename.split(os.sep)[-1][15] # H or V - self.annotationXML = {} - self.calibrationXML = {} - self.noiseXML = {} - - if zipfile.is_zipfile(self.filename): - with zipfile.PyZipFile(self.filename) as zf: - annotationFiles = [fn for fn in zf.namelist() if 'annotation/s1' in fn] - calibrationFiles = [fn for fn in zf.namelist() - if 'annotation/calibration/calibration-s1' in fn] - noiseFiles = [fn for fn in zf.namelist() if 'annotation/calibration/noise-s1' in fn] - - for pol in [txPol + 'H', txPol + 'V']: - self.annotationXML[pol] = BeautifulSoup( - [zf.read(fn) for fn in annotationFiles if pol.lower() in fn][0], - features="xml") - self.calibrationXML[pol] = parseString( - [zf.read(fn) for fn in calibrationFiles if pol.lower() in fn][0]) - self.noiseXML[pol] = BeautifulSoup( - [zf.read(fn) for fn in noiseFiles if pol.lower() in fn][0], features="xml") - self.manifestXML = parseString(zf.read([fn for fn in zf.namelist() - if 'manifest.safe' in fn][0])) - else: - annotationFiles = [fn for fn in glob.glob(self.filename+'/annotation/*') if 's1' in fn] - calibrationFiles = [fn for fn in glob.glob(self.filename+'/annotation/calibration/*') - if 'calibration-s1' in fn] - noiseFiles = [fn for fn in glob.glob(self.filename+'/annotation/calibration/*') - if 'noise-s1' in fn] - - for pol in [txPol + 'H', txPol + 'V']: - - for fn in annotationFiles: - if pol.lower() in fn: - with open(fn) as ff: - self.annotationXML[pol] = BeautifulSoup(ff.read(), features="xml") - - for fn in calibrationFiles: - if pol.lower() in fn: - with open(fn) as ff: - self.calibrationXML[pol] = parseString(ff.read()) - - for fn in noiseFiles: - if pol.lower() in fn: - with open(fn) as ff: - self.noiseXML[pol] = BeautifulSoup(ff.read(), features="xml") - - with open(glob.glob(self.filename+'/manifest.safe')[0]) as ff: - self.manifestXML = parseString(ff.read()) - # scene center time will be used as the reference for relative azimuth time in seconds self.time_coverage_center = ( self.time_coverage_start + timedelta( seconds=(self.time_coverage_end - self.time_coverage_start).total_seconds()/2) ) + self.xml = Sentinel1ImageXml(self.filename) # get processor version of Sentinel-1 IPF (Instrument Processing Facility) - self.IPFversion = float(self.manifestXML.getElementsByTagName('safe:software')[0] - .attributes['version'].value) + self.IPFversion = float(self.xml.manifest.find('safe:software').attrs['version']) if self.IPFversion < 2.43: print('\nERROR: IPF version of input image is lower than 2.43! ' 'Denoising vectors in annotation files are not qualified. ' @@ -122,15 +172,6 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): elif 2.43 <= self.IPFversion < 2.53: print('\nWARNING: IPF version of input image is lower than 2.53! ' 'ESA default noise correction result might be wrong.\n') - # get the auxiliary calibration file - resourceList = self.manifestXML.getElementsByTagName('resource') - resourceList += self.manifestXML.getElementsByTagName('safe:resource') - for resource in resourceList: - if resource.attributes['role'].value=='AUX_CAL': - auxCalibFilename = resource.attributes['name'].value.split('/')[-1] - self.set_aux_data_dir() - self.download_aux_calibration(auxCalibFilename, self.platform.lower()) - self.auxiliaryCalibrationXML = parse(self.auxiliaryCalibration_file) @cached_property def scallopingGain(self, pol='HV'): @@ -154,7 +195,7 @@ def swath_bounds(self): swath_bounds = {} for pol in self.pols: swath_bounds[pol] = {} - for swathMerge in self.annotationXML[pol].find_all('swathMerge'): + for swathMerge in self.xml.annotation[pol].find_all('swathMerge'): swath_bounds[pol][swathMerge.swath.text] = defaultdict(list) for swathBounds in swathMerge.find_all('swathBounds'): for name in names: @@ -178,7 +219,7 @@ def geolocation(self): geolocation = {} for pol in self.pols: geolocation[pol] = defaultdict(list) - for p in self.annotationXML[pol].find_all('geolocationGridPoint'): + for p in self.xml.annotation[pol].find_all('geolocationGridPoint'): for c in p: if c.name: geolocation[pol][c.name].append(geolocation_keys[c.name](c.text)) @@ -205,9 +246,8 @@ def geolocation_relative_azimuth_time(self): def calibration(self): calibration = {} for pol in self.pols: - soup = BeautifulSoup(self.calibrationXML[pol].toxml(), features="xml") calibration[pol] = defaultdict(list) - for cv in soup.find_all('calibrationVector'): + for cv in self.xml.calibration[pol].find_all('calibrationVector'): for n in cv: if n.name: calibration[pol][n.name].append(n.text) @@ -218,6 +258,26 @@ def calibration(self): calibration[pol][key] = np.array([list(map(float, p.split())) for p in calibration[pol][key]]) return calibration + @cached_property + def aux_calibration_params(self): + swaths = [f'{self.obsMode}{li}' for li in self.swath_ids] + calibration_params = { + 'HH': {swath: {} for swath in swaths}, + 'HV': {swath: {} for swath in swaths} + } + for calibrationParams in self.xml.auxiliary.find_all('calibrationParams'): + swath = calibrationParams.swath.text + pol = calibrationParams.polarisation.text + if pol in calibration_params and swath in calibration_params[pol]: + calibration_params[pol][swath]['elevationAngleIncrement'] = float(calibrationParams.elevationAntennaPattern.elevationAngleIncrement.text) + calibration_params[pol][swath]['azimuthAngleIncrement'] = float(calibrationParams.azimuthAntennaElementPattern.azimuthAngleIncrement.text) + calibration_params[pol][swath]['absoluteCalibrationConstant'] = float(calibrationParams.absoluteCalibrationConstant.text) + calibration_params[pol][swath]['noiseCalibrationFactor'] = float(calibrationParams.noiseCalibrationFactor.text) + calibration_params[pol][swath]['elevationAntennaPatternCount'] = int(calibrationParams.elevationAntennaPattern.values.attrs['count']) + calibration_params[pol][swath]['elevationAntennaPattern'] = np.array([float(i) for i in calibrationParams.elevationAntennaPattern.values.text.split()]) + calibration_params[pol][swath]['azimuthAntennaPattern'] = np.array([float(i) for i in calibrationParams.azimuthAntennaElementPattern.values.text.split()]) + return calibration_params + @cache def noise_range(self, pol): if self.IPFversion < 2.9: @@ -227,8 +287,7 @@ def noise_range(self, pol): noiseRangeVectorName = 'noiseRangeVector' noiseLutName = 'noiseRangeLut' noise_range = defaultdict(list) - #soup = BeautifulSoup(self.noiseXML[pol].toxml(), features="xml") - for noiseVector in self.noiseXML[pol].find_all(noiseRangeVectorName): + for noiseVector in self.xml.noise[pol].find_all(noiseRangeVectorName): noise_range['azimuthTime'].append(parse_azimuth_time(noiseVector.azimuthTime.text)) noise_range['line'].append(int(noiseVector.line.text)) noise_range['pixel'].append(np.array([int(i) for i in noiseVector.pixel.text.split()])) @@ -250,8 +309,7 @@ def noise_azimuth(self, pol): noise = [np.array([1.0, 1.0])]) else: int_names = ['firstAzimuthLine', 'firstRangeSample', 'lastAzimuthLine', 'lastRangeSample'] - #soup = BeautifulSoup(self.noiseXML[pol].toxml(), features="xml") - for noiseAzimuthVector in self.noiseXML[pol].find_all('noiseAzimuthVector'): + for noiseAzimuthVector in self.xml.noise[pol].find_all('noiseAzimuthVector'): swath = noiseAzimuthVector.swath.text for int_name in int_names: noise_azimuth[swath][int_name].append(int(noiseAzimuthVector.find(int_name).text)) @@ -259,47 +317,6 @@ def noise_azimuth(self, pol): noise_azimuth[swath]['noise'].append(np.array([float(i) for i in noiseAzimuthVector.noiseAzimuthLut.text.split()])) return noise_azimuth - def set_aux_data_dir(self): - """ Set directory where aux calibration data is stored """ - self.aux_data_dir = os.path.join(os.environ.get('XDG_DATA_HOME', os.path.expanduser('~')), - '.s1denoise') - if not os.path.exists(self.aux_data_dir): - os.makedirs(self.aux_data_dir) - - def download_aux_calibration(self, filename, platform): - self.auxiliaryCalibration_file = os.path.join(self.aux_data_dir, filename, 'data', '%s-aux-cal.xml' % platform) - if os.path.exists(self.auxiliaryCalibration_file): - return - vs = filename.split('_')[3].lstrip('V') - validity_start = f'{vs[:4]}-{vs[4:6]}-{vs[6:8]}T{vs[9:11]}:{vs[11:13]}:{vs[13:15]}' - - cd = filename.split('_')[4].lstrip('G') - creation_date = f'{cd[:4]}-{cd[4:6]}-{cd[6:8]}T{cd[9:11]}:{cd[11:13]}:{cd[13:15]}' - def get_remote_url(api_url): - with requests.get(api_url, stream=True) as r: - rjson = json.loads(r.content.decode()) - remote_url = rjson['results'][0]['remote_url'] - physical_name = rjson['results'][0]['physical_name'] - return remote_url, physical_name - - try: - remote_url, physical_name = get_remote_url(f'https://sar-mpc.eu/api/v1/?product_type=AUX_CAL&validity_start={validity_start}&creation_date={creation_date}') - except: - remote_url, physical_name = get_remote_url(f'https://sar-mpc.eu/api/v1/?product_type=AUX_CAL&validity_start={validity_start}') - - download_file = os.path.join(self.aux_data_dir, physical_name) - print(f'downloading {filename}.zip from {remote_url}') - with requests.get(remote_url, stream=True) as r: - with open(download_file, "wb") as f: - f.write(r.content) - - with zipfile.ZipFile(download_file, 'r') as download_zip: - download_zip.extractall(path=self.aux_data_dir) - output_dir = f'{self.aux_data_dir}/{filename}' - if not os.path.exists(output_dir): - # in case the physical_name of the downloaded file does not exactly match the filename from manifest - # the best found AUX-CAL file is copied to the filename from manifest - shutil.copytree(download_file.rstrip('.zip'), output_dir) def get_swath_id_vectors(self, pol, pixel=None): if pixel is None: @@ -352,7 +369,7 @@ def get_pg_product(self, pol, pg_name='pgProductAmplitude'): azimuth_time = [(t - self.time_coverage_center).total_seconds() for t in self.noise_range(pol)['azimuthTime']] pg = defaultdict(dict) - for pgpa in self.annotationXML[pol].find_all(pg_name): + for pgpa in self.xml.annotation[pol].find_all(pg_name): pg[pgpa.parent.parent.parent.swath.text][pgpa.parent.azimuthTime.text] = float(pgpa.text) pg_swaths = {} @@ -475,10 +492,9 @@ def get_eap_interpolator(self, subswathID, pol): It computes EAP for input boresight angles """ - elevationAntennaPatternLUT = self.import_elevationAntennaPattern(pol) - eap_lut = np.array(elevationAntennaPatternLUT[subswathID]['elevationAntennaPattern']) - recordLength = elevationAntennaPatternLUT[subswathID]['elevationAntennaPatternCount'] - eai_lut = elevationAntennaPatternLUT[subswathID]['elevationAngleIncrement'] + eap_lut = self.aux_calibration_params[pol][subswathID]['elevationAntennaPattern'] + recordLength = self.aux_calibration_params[pol][subswathID]['elevationAntennaPatternCount'] + eai_lut = self.aux_calibration_params[pol][subswathID]['elevationAngleIncrement'] if recordLength == eap_lut.shape[0]: # in case if elevationAntennaPattern is given in dB amplitudeLUT = 10**(eap_lut/10) @@ -488,6 +504,7 @@ def get_eap_interpolator(self, subswathID, pol): angleLUT = np.arange(-(recordLength//2),+(recordLength//2)+1) * eai_lut eap_interpolator = InterpolatedUnivariateSpline(angleLUT, np.sqrt(amplitudeLUT)) return eap_interpolator + def get_boresight_angle_interpolator(self, pol): """ Prepare interpolator for boresight angles. It computes BA for input x,y coordinates. """ @@ -518,7 +535,7 @@ def get_range_spread_loss_interpolator(self, pol, rsl_power=3./2.): rsl_power : float power for RSL = (2 * Rref / time / C)^rsl_power """ - referenceRange = float(self.annotationXML[pol].find('referenceRange').text) + referenceRange = float(self.xml.annotation[pol].find('referenceRange').text) rangeSpreadingLoss = (referenceRange / self.geolocation[pol]['slantRangeTime'] / SPEED_OF_LIGHT * 2)**rsl_power rsp_interpolator = RectBivariateSpline( self.geolocation[pol]['line'], self.geolocation[pol]['pixel'], rangeSpreadingLoss) @@ -575,7 +592,6 @@ def get_shifted_noise_vectors(self, pol, pixel=None, noise=None, skip=4, min_val pixel_shift = minimize(cost, 0, args=(valid_pix[skip:-skip], noise_interpolator, apg[skip:-skip])).x[0] noise_shifted0 = noise_interpolator(valid_pix + pixel_shift) noise_shifted[v1][valid2] = noise_shifted0 - return noise_shifted def get_corrected_noise_vectors(self, pol, nesz, pixel=None, add_pb=True): @@ -1022,37 +1038,11 @@ def remove_thermal_noise(self, pol, algorithm='NERSC', remove_negative=True, min sigma0 = fill_gaps(sigma0, sigma0 <= 0) return sigma0 - def import_elevationAntennaPattern(self, pol): - ''' Import elevation antenna pattern from auxiliary calibration XML DOM ''' - calParamsList = self.auxiliaryCalibrationXML.getElementsByTagName('calibrationParams') - elevationAntennaPattern = { '%s%s' % (self.obsMode, li): - { 'elevationAngleIncrement':[], - 'elevationAntennaPattern':[], - 'absoluteCalibrationConstant':[], - 'noiseCalibrationFactor':[], - 'elevationAntennaPatternCount': None - } - for li in self.swath_ids } - for iList in calParamsList: - swath = get_DOM_nodeValue(iList,['swath']) - pol = get_DOM_nodeValue(iList,['polarisation']) - if (swath in elevationAntennaPattern.keys()) and (pol==pol): - elem = iList.getElementsByTagName('elevationAntennaPattern')[0] - for k in elevationAntennaPattern[swath].keys(): - if k=='elevationAngleIncrement': - elevationAntennaPattern[swath][k] = get_DOM_nodeValue(elem,[k],'float') - elif k=='elevationAntennaPattern': - elevationAntennaPattern[swath][k] = get_DOM_nodeValue(elem,['values'],'float') - elevationAntennaPattern[swath]['elevationAntennaPatternCount'] = int(elem.getElementsByTagName('values')[0].getAttribute('count')) - elif k in ['absoluteCalibrationConstant', 'noiseCalibrationFactor']: - elevationAntennaPattern[swath][k] = get_DOM_nodeValue(iList,[k],'float') - return elevationAntennaPattern - @cache def antenna_pattern(self, pol): list_keys = ['slantRangeTime', 'elevationAngle', 'elevationPattern', 'incidenceAngle'] antenna_pattern = {} - antennaPatternList = self.annotationXML[pol].find('antennaPatternList') + antennaPatternList = self.xml.annotation[pol].find('antennaPatternList') compute_roll = True for antennaPattern in antennaPatternList.find_all('antennaPattern'): swath = antennaPattern.swath.text @@ -1076,7 +1066,7 @@ def import_orbit(self, pol): orbit = { 'time':[], 'position':{'x':[], 'y':[], 'z':[]}, 'velocity':{'x':[], 'y':[], 'z':[]} } - for o in self.annotationXML[pol].find('orbitList').find_all('orbit'): + for o in self.xml.annotation[pol].find('orbitList').find_all('orbit'): orbit['time'].append(parse_azimuth_time(o.time.text)) for name1 in ['position', 'velocity']: for name2 in ['x', 'y', 'z']: @@ -1243,32 +1233,6 @@ def subswathIndexMap(self, pol): subswathIndexMap[fal:lal+1,frs:lrs+1] = iSW return subswathIndexMap - def import_azimuthAntennaElementPattern(self, pol): - ''' Import azimuth antenna element pattern from auxiliary calibration XML DOM ''' - calParamsList = self.auxiliaryCalibrationXML.getElementsByTagName('calibrationParams') - azimuthAntennaElementPattern = { '%s%s' % (self.obsMode, li): - { 'azimuthAngleIncrement':[], - 'azimuthAntennaElementPattern':[], - 'absoluteCalibrationConstant':[], - 'noiseCalibrationFactor':[] } - for li in range(1, {'IW':3, 'EW':5}[self.obsMode]+1) } - for iList in calParamsList: - swath = get_DOM_nodeValue(iList,['swath']) - pol = get_DOM_nodeValue(iList,['polarisation']) - if (swath in azimuthAntennaElementPattern.keys()) and (pol==pol): - elem = iList.getElementsByTagName('azimuthAntennaElementPattern')[0] - for k in azimuthAntennaElementPattern[swath].keys(): - if k=='azimuthAngleIncrement': - azimuthAntennaElementPattern[swath][k] = ( - get_DOM_nodeValue(elem,[k],'float') ) - elif k=='azimuthAntennaElementPattern': - azimuthAntennaElementPattern[swath][k] = ( - get_DOM_nodeValue(elem,['values'],'float') ) - else: - azimuthAntennaElementPattern[swath][k] = ( - get_DOM_nodeValue(iList,[k],'float') ) - return azimuthAntennaElementPattern - def subswathCenterSampleIndex(self, pol): ''' Range center pixel indices along azimuth for each subswath ''' swathBounds = self.swath_bounds[pol] @@ -1318,7 +1282,7 @@ def azimuthFmRateAtGivenTime(self, pol, relativeAzimuthTime, slantRangeTime): def import_azimuthFmRate(self, pol): ''' Import azimuth frequency modulation rate from annotation XML DOM ''' azimuthFmRate = defaultdict(list) - for afmr in self.annotationXML[pol].find_all('azimuthFmRate'): + for afmr in self.xml.annotation[pol].find_all('azimuthFmRate'): azimuthFmRate['azimuthTime'].append(datetime.strptime(afmr.azimuthTime.text, '%Y-%m-%dT%H:%M:%S.%f')) azimuthFmRate['t0'].append(float(afmr.t0.text)) if 'azimuthFmRatePolynomial' in afmr.decode(): @@ -1337,12 +1301,12 @@ def focusedBurstLengthInTime(self, pol): focusedBurstLengthInTime : dict one values for each subswath (different for IW and EW) ''' - azimuthFrequency = float(self.annotationXML[pol].find('azimuthFrequency').text) + azimuthFrequency = float(self.xml.annotation[pol].find('azimuthFrequency').text) azimuthTimeIntevalInSLC = 1. / azimuthFrequency focusedBurstLengthInTime = {} # nominalLinesPerBurst should be smaller than the real values nominalLinesPerBurst = {'IW':1450, 'EW':1100}[self.obsMode] - for inputDimensions in self.annotationXML[pol].find_all('inputDimensions'): + for inputDimensions in self.xml.annotation[pol].find_all('inputDimensions'): swath = inputDimensions.swath.text numberOfInputLines = int(inputDimensions.numberOfInputLines.text) numberOfBursts = max( @@ -1357,10 +1321,9 @@ def focusedBurstLengthInTime(self, pol): def get_subswathScallopingGain(self, pol, subswathID): # azimuth antenna element patterns (AAEP) lookup table for given subswath - AAEP = self.import_azimuthAntennaElementPattern(pol)[subswathID] - gainAAEP = np.array(AAEP['azimuthAntennaElementPattern']) - angleAAEP = ( np.arange(-(len(gainAAEP)//2), len(gainAAEP)//2+1) - * AAEP['azimuthAngleIncrement'] ) + gainAAEP = self.aux_calibration_params[pol][subswathID]['azimuthAntennaPattern'] + azimuthAngleIncrement = self.aux_calibration_params[pol][subswathID]['azimuthAngleIncrement'] + angleAAEP = np.arange(-(len(gainAAEP)//2), len(gainAAEP)//2+1) * azimuthAngleIncrement # subswath range center pixel index subswathCenterSampleIndex = self.subswathCenterSampleIndex(pol)[subswathID] # slant range time along subswath range center diff --git a/s1denoise/utils.py b/s1denoise/utils.py index ed5949f..3acb35e 100644 --- a/s1denoise/utils.py +++ b/s1denoise/utils.py @@ -39,27 +39,6 @@ def fit_noise_scaling_coeff(meanS0, meanN0, pixelIndex): w=weight, deg=1, full=True)[1].item() return scalingFactor, correlationCoefficient, fitResidual -def get_DOM_nodeValue(element, tags, oType='str'): - ''' Get value of XML DOM sub-element based on tags ''' - if oType not in ['str', 'int', 'float']: - raise ValueError('see error message.') - value = get_DOM_subElement(element, tags).childNodes[0].nodeValue.split() - if oType == 'str': - value = [str(v) for v in value] - elif oType == 'int': - value = [int(round(float(v))) for v in value] - elif oType == 'float': - value = [float(v) for v in value] - if len(value)==1: - value = value[0] - return value - -def get_DOM_subElement(element, tags): - ''' Get sub-element from XML DOM element based on tags ''' - for tag in tags: - element = element.getElementsByTagName(tag)[0] - return element - def fill_gaps(array, mask, distance=15): """ Fill gaps in input raster From 81009a84abf1695fc0bb6096faba31219f0ec3e4 Mon Sep 17 00:00:00 2001 From: akorosov Date: Mon, 11 Dec 2023 15:56:51 +0100 Subject: [PATCH 19/25] refactor scalloping_gain --- s1denoise/sentinel1image.py | 16 ++++------------ 1 file changed, 4 insertions(+), 12 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index baba018..cde5963 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -173,14 +173,6 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): print('\nWARNING: IPF version of input image is lower than 2.53! ' 'ESA default noise correction result might be wrong.\n') - @cached_property - def scallopingGain(self, pol='HV'): - sg = {} - for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): - subswathID = '%s%s' % (self.obsMode, iSW) - sg[subswathID] = self.get_subswathScallopingGain(pol, subswathID) - return sg - @cached_property def swath_bounds(self): """ Boundaries of blocks in each swath for each polarisation """ @@ -191,7 +183,6 @@ def swath_bounds(self): 'lastAzimuthLine' : int, 'lastRangeSample' : int, } - swath_bounds = {} for pol in self.pols: swath_bounds[pol] = {} @@ -1319,7 +1310,8 @@ def focusedBurstLengthInTime(self, pol): raise ValueError('number of bursts cannot be determined.') return focusedBurstLengthInTime - def get_subswathScallopingGain(self, pol, subswathID): + @cache + def scalloping_gain(self, pol, subswathID): # azimuth antenna element patterns (AAEP) lookup table for given subswath gainAAEP = self.aux_calibration_params[pol][subswathID]['azimuthAntennaPattern'] azimuthAngleIncrement = self.aux_calibration_params[pol][subswathID]['azimuthAngleIncrement'] @@ -1387,7 +1379,7 @@ def get_noise_azimuth_vectors(self, pol, pixel=None): for i, l in enumerate(line): for j in range(1,6): gpi = swath_idvs[i] == j - scall[i][gpi] = self.scallopingGain[f'EW{j}'][l] + scall[i][gpi] = self.scalloping_gain(pol, f'EW{j}')[l] return scall noiseAzimuthVector = self.noise_azimuth(pol) @@ -1419,7 +1411,7 @@ def get_scalloping_full_size(self, pol): subswathIndexMap = self.subswathIndexMap(pol) for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): subswathID = '%s%s' % (self.obsMode, iSW) - scallopingGain = self.scallopingGain[subswathID] + scallopingGain = self.scalloping_gain(pol, subswathID) # assign computed scalloping gain into each subswath valid = (subswathIndexMap==iSW) scall_fs[valid] = ( From a715eec8a5e01833891320beccd7cd89c26918c1 Mon Sep 17 00:00:00 2001 From: akorosov Date: Mon, 11 Dec 2023 15:59:44 +0100 Subject: [PATCH 20/25] fix hardcoded EW --- s1denoise/sentinel1image.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index cde5963..3aee80d 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -308,7 +308,6 @@ def noise_azimuth(self, pol): noise_azimuth[swath]['noise'].append(np.array([float(i) for i in noiseAzimuthVector.noiseAzimuthLut.text.split()])) return noise_azimuth - def get_swath_id_vectors(self, pol, pixel=None): if pixel is None: pixel = self.noise_range(pol)['pixel'] @@ -384,7 +383,7 @@ def get_tg_vectors(self, pol, min_size=3): for i, gtot in enumerate(tg_vectors): for j in range(1,6): gpi = swath_ids[i] == j - gtot[gpi] *= pg_swaths[f'EW{j}'][i] + gtot[gpi] *= pg_swaths[f'{self.obsMode}{j}'][i] return tg_vectors def get_tg_scales_offsets(self): @@ -1379,9 +1378,8 @@ def get_noise_azimuth_vectors(self, pol, pixel=None): for i, l in enumerate(line): for j in range(1,6): gpi = swath_idvs[i] == j - scall[i][gpi] = self.scalloping_gain(pol, f'EW{j}')[l] + scall[i][gpi] = self.scalloping_gain(pol, f'{self.obsMode}{j}')[l] return scall - noiseAzimuthVector = self.noise_azimuth(pol) for iSW in self.swath_ids: swath = f'{self.obsMode}{iSW}' From e73da06eabd9001de4afe2db7f54a83835c2b4f5 Mon Sep 17 00:00:00 2001 From: akorosov Date: Tue, 12 Dec 2023 09:09:31 +0100 Subject: [PATCH 21/25] refactor: Nansat; geolocation_interpolator --- s1denoise/sentinel1image.py | 136 ++++++++++++++++++------------------ s1denoise/tools.py | 1 - setup.py | 1 + 3 files changed, 70 insertions(+), 68 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index 3aee80d..d528e99 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -10,7 +10,6 @@ import zipfile from functools import cached_property, cache -from nansat import Nansat import numpy as np from osgeo import gdal from bs4 import BeautifulSoup @@ -40,28 +39,22 @@ 'EW5': 2.122855427 } # degrees per second. Available from AUX_INS class Sentinel1ImageXml: - def __init__(self, filename): - self.txPol = filename.split(os.sep)[-1][15] # H or V - self.platform = filename.split(os.sep)[-1][:3] # S1A or S1B - - # get list of all filenames - if zipfile.is_zipfile(filename): - with zipfile.PyZipFile(filename) as zf: - filenames = zf.namelist() - else: - filenames = [str(i) for i in Path(f'{filename}/').rglob('*')] - manifest_file = [f for f in filenames if 'manifest.safe' in f][0] + def __init__(self, s1): + ''' Read calibration/annotation XML files and auxiliary XML file ''' + self.txPol = s1.filename.split(os.sep)[-1][15] # H or V + self.platform = s1.filename.split(os.sep)[-1][:3] # S1A or S1B + manifest_file = [f for f in s1.filenames if 'manifest.safe' in f][0] # find annotation, calibration and noise files self.xml = dict(annotation = {}, calibration = {}, noise = {}) for pol in [self.txPol + 'H', self.txPol + 'V']: - self.xml['annotation'][pol] = [f for f in filenames if ('annotation/s1' in f and pol.lower() in f)][0] - self.xml['calibration'][pol] = [f for f in filenames if ('calibration-s1' in f and pol.lower() in f)][0] - self.xml['noise'][pol] = [f for f in filenames if ('noise-s1' in f and pol.lower() in f)][0] + self.xml['annotation'][pol] = [f for f in s1.filenames if ('annotation/s1' in f and pol.lower() in f)][0] + self.xml['calibration'][pol] = [f for f in s1.filenames if ('calibration-s1' in f and pol.lower() in f)][0] + self.xml['noise'][pol] = [f for f in s1.filenames if ('noise-s1' in f and pol.lower() in f)][0] # read and parse XML files - if zipfile.is_zipfile(filename): - with zipfile.PyZipFile(filename) as zf: + if zipfile.is_zipfile(s1.filename): + with zipfile.PyZipFile(s1.filename) as zf: for name in self.xml: for pol in self.xml[name]: self.xml[name][pol] = BeautifulSoup(zf.read(self.xml[name][pol]), features="xml") @@ -141,12 +134,17 @@ def auxiliary(self): return self.xml['auxiliary'] -class Sentinel1Image(Nansat): - """ Cal/Val routines for Sentinel-1 performed on range noise vector coordinatess""" +class Sentinel1Image(): + """ Thermal noise correction for S1 GRD data""" + def __init__(self, filename): + self.filename = filename + self.find_filesnames() + # get list of measurements + self.measurements = {os.path.basename(f).split('-')[3].upper() : f for f in self.filenames if 'measurement/s1' in f} + if self.is_zipfile: + for pol in self.measurements: + self.measurements[pol] = f'/vsizip/{filename}/{self.measurements[pol]}' - def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): - ''' Read calibration/annotation XML files and auxiliary XML file ''' - Nansat.__init__(self, filename, mapper=mapper, log_level=log_level, fast=fast) if ( self.filename.split(os.sep)[-1][4:16] not in [ 'IW_GRDH_1SDH', 'IW_GRDH_1SDV', @@ -162,7 +160,7 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): # scene center time will be used as the reference for relative azimuth time in seconds self.time_coverage_center = ( self.time_coverage_start + timedelta( seconds=(self.time_coverage_end - self.time_coverage_start).total_seconds()/2) ) - self.xml = Sentinel1ImageXml(self.filename) + self.xml = Sentinel1ImageXml(self) # get processor version of Sentinel-1 IPF (Instrument Processing Facility) self.IPFversion = float(self.xml.manifest.find('safe:software').attrs['version']) if self.IPFversion < 2.43: @@ -173,6 +171,28 @@ def __init__(self, filename, mapper='sentinel1_l1', log_level=30, fast=False): print('\nWARNING: IPF version of input image is lower than 2.53! ' 'ESA default noise correction result might be wrong.\n') + def find_filesnames(self): + self.is_zipfile = False + if zipfile.is_zipfile(self.filename): + self.is_zipfile = True + with zipfile.PyZipFile(self.filename) as zf: + self.filenames = zf.namelist() + else: + self.filenames = [str(i) for i in Path(f'{self.filename}/').rglob('*')] + + @cached_property + def time_coverage_start(self): + return datetime.strptime(os.path.basename(self.filename).split('_')[4], '%Y%m%dT%H%M%S') + + @cached_property + def time_coverage_end(self): + return datetime.strptime(os.path.basename(self.filename).split('_')[5], '%Y%m%dT%H%M%S') + + @cached_property + def shape(self): + a = self.xml.annotation[self.pols[0]] + return [int(a.find(i).text) for i in ['numberOfLines', 'numberOfSamples']] + @cached_property def swath_bounds(self): """ Boundaries of blocks in each swath for each polarisation """ @@ -294,9 +314,9 @@ def noise_azimuth(self, pol): noise_azimuth[f'{self.obsMode}{swid}'] = dict( firstAzimuthLine = [0], firstRangeSample = [0], - lastAzimuthLine = [self.shape()[0]-1], - lastRangeSample = [self.shape()[1]-1], - line = [np.array([0, self.shape()[0]-1])], + lastAzimuthLine = [self.shape[0]-1], + lastRangeSample = [self.shape[1]-1], + line = [np.array([0, self.shape[0]-1])], noise = [np.array([1.0, 1.0])]) else: int_names = ['firstAzimuthLine', 'firstRangeSample', 'lastAzimuthLine', 'lastRangeSample'] @@ -431,22 +451,17 @@ def get_swath_interpolator(self, pol, swath_name, line, pixel, z): z_interp2 = RectBivariateSpline(swath_lines, pix_vec_fr, np.array(z_vecs)) return z_interp2, swath_coords - def get_elevation_incidence_angle_interpolators(self, p): - return ( - RectBivariateSpline( - self.geolocation[p]['line'], - self.geolocation[p]['pixel'], - self.geolocation[p]['elevationAngle']), - RectBivariateSpline( - self.geolocation[p]['line'], - self.geolocation[p]['pixel'], - self.geolocation[p]['incidenceAngle']) - ) - - def get_elevation_incidence_angle_vectors(self, ea_interpolator, ia_interpolator, line, pixel): - ea = [ea_interpolator(l, p).flatten() for (l, p) in zip(line, pixel)] - ia = [ia_interpolator(l, p).flatten() for (l, p) in zip(line, pixel)] - return ea, ia + def geolocation_interpolator(self, pol, z): + return RectBivariateSpline( + self.geolocation[pol]['line'], + self.geolocation[pol]['pixel'], + z) + + def get_angle_vectors(self, pol, angle_name): + z = self.geolocation[pol][angle_name] + i = self.geolocation_interpolator(pol, z) + angle_vectors = [i(l, p).flatten() for (l, p) in zip(line, pixel)] + return angle_vectors def get_calibration_vectors(self, pol, name='sigmaNought'): line = self.noise_range(pol)['line'] @@ -516,8 +531,7 @@ def get_boresight_angle_interpolator(self, pol): roll_map = rollAngleIntp(self.geolocation_relative_azimuth_time[pol]) boresight_map = self.geolocation[pol]['elevationAngle'] - roll_map - boresight_angle_interpolator = RectBivariateSpline( - self.geolocation[pol]['line'], self.geolocation[pol]['pixel'], boresight_map) + boresight_angle_interpolator = self.geolocation_interpolator(pol, boresight_map) return boresight_angle_interpolator def get_range_spread_loss_interpolator(self, pol, rsl_power=3./2.): @@ -527,8 +541,7 @@ def get_range_spread_loss_interpolator(self, pol, rsl_power=3./2.): """ referenceRange = float(self.xml.annotation[pol].find('referenceRange').text) rangeSpreadingLoss = (referenceRange / self.geolocation[pol]['slantRangeTime'] / SPEED_OF_LIGHT * 2)**rsl_power - rsp_interpolator = RectBivariateSpline( - self.geolocation[pol]['line'], self.geolocation[pol]['pixel'], rangeSpreadingLoss) + rsp_interpolator = self.geolocation_interpolator(pol, rangeSpreadingLoss) return rsp_interpolator def get_shifted_noise_vectors(self, pol, pixel=None, noise=None, skip=4, min_valid_size=10, rsl_power=3./2.): @@ -917,11 +930,10 @@ def experiment_powerBalancing(self, pol, average_lines=777, zoom_step=2): def interp_nrv_full_size(self, z, line, pixel, pol, power=1): """ Interpolate noise range vectors to full size """ - z_fs = np.zeros(self.shape()) + np.nan + z_fs = np.zeros(self.shape) + np.nan swath_names = [f'{self.obsMode}{i}' for i in self.swath_ids] for swath_name in swath_names: - z_interp2, swath_coords = self.get_swath_interpolator( - pol, swath_name, line, pixel, z) + z_interp2, swath_coords = self.get_swath_interpolator(pol, swath_name, line, pixel, z) for fal, lal, frs, lrs in zip(*swath_coords): pix_vec_fr = np.arange(frs, lrs+1) lin_vec_fr = np.arange(fal, lal+1) @@ -954,7 +966,7 @@ def get_corrected_nesz_full_size(self, pol, nesz): def get_raw_sigma0_full_size(self, pol, min_dn=0): """ Read DN from input GeoTiff file and calibrate """ - src_filename = self.bands()[self.get_band_number(f'DN_{pol}')]['SourceFilename'] + src_filename = self.measurements[pol] ds = gdal.Open(src_filename) dn = ds.ReadAsArray() @@ -1212,7 +1224,7 @@ def remove_texture_noise(self, pol, window=3, weight=0.1, s0_min=0, remove_negat def subswathIndexMap(self, pol): ''' Convert subswath indices into full grid pixels ''' - subswathIndexMap = np.zeros(self.shape(), dtype=np.uint8) + subswathIndexMap = np.zeros(self.shape, dtype=np.uint8) for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): swathBound = self.swath_bounds[pol]['%s%s' % (self.obsMode, iSW)] zipped = zip(swathBound['firstAzimuthLine'], @@ -1237,16 +1249,6 @@ def subswathCenterSampleIndex(self, pol): np.sum(midPixelIndices * numberOfLines) / np.sum(numberOfLines) )) return subswathCenterSampleIndex - def geolocationGridPointInterpolator(self, pol, itemName): - ''' Generate interpolator for items in geolocation grid point list ''' - if itemName == 'azimuthTime': - z = self.geolocation_relative_azimuth_time[pol] - else: - z = self.geolocation[pol][itemName] - interpolator = RectBivariateSpline( - self.geolocation[pol]['line'], self.geolocation[pol]['pixel'], z) - return interpolator - def azimuthFmRateAtGivenTime(self, pol, relativeAzimuthTime, slantRangeTime): ''' Get azimuth frequency modulation rate for given time vectors @@ -1318,11 +1320,11 @@ def scalloping_gain(self, pol, subswathID): # subswath range center pixel index subswathCenterSampleIndex = self.subswathCenterSampleIndex(pol)[subswathID] # slant range time along subswath range center - interpolator = self.geolocationGridPointInterpolator(pol, 'slantRangeTime') - slantRangeTime = np.squeeze(interpolator(np.arange(self.shape()[0]), subswathCenterSampleIndex)) + interpolator = self.geolocation_interpolator(pol, self.geolocation[pol]['slantRangeTime']) + slantRangeTime = np.squeeze(interpolator(np.arange(self.shape[0]), subswathCenterSampleIndex)) # relative azimuth time along subswath range center - interpolator = self.geolocationGridPointInterpolator(pol, 'azimuthTime') - azimuthTime = np.squeeze(interpolator(np.arange(self.shape()[0]), subswathCenterSampleIndex)) + interpolator = self.geolocation_interpolator(pol, self.geolocation_relative_azimuth_time[pol]) + azimuthTime = np.squeeze(interpolator(np.arange(self.shape[0]), subswathCenterSampleIndex)) # Doppler rate induced by satellite motion motionDopplerRate = self.azimuthFmRateAtGivenTime(pol, azimuthTime, slantRangeTime) # antenna steering rate @@ -1404,7 +1406,7 @@ def get_noise_azimuth_vectors(self, pol, pixel=None): def get_scalloping_full_size(self, pol): """ Interpolate noise azimuth vector to full resolution for all blocks """ - scall_fs = np.zeros(self.shape()) + scall_fs = np.zeros(self.shape) if self.IPFversion < 2.9: subswathIndexMap = self.subswathIndexMap(pol) for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): @@ -1413,7 +1415,7 @@ def get_scalloping_full_size(self, pol): # assign computed scalloping gain into each subswath valid = (subswathIndexMap==iSW) scall_fs[valid] = ( - scallopingGain[:, np.newaxis] * np.ones((1,self.shape()[1])))[valid] + scallopingGain[:, np.newaxis] * np.ones((1,self.shape[1])))[valid] return scall_fs noiseAzimuthVector = self.noise_azimuth(pol) diff --git a/s1denoise/tools.py b/s1denoise/tools.py index e664524..5f6d0f9 100644 --- a/s1denoise/tools.py +++ b/s1denoise/tools.py @@ -5,7 +5,6 @@ import json import os -from nansat import Nansat import numpy as np from s1denoise import Sentinel1Image diff --git a/setup.py b/setup.py index dfcb7aa..b1e5908 100644 --- a/setup.py +++ b/setup.py @@ -28,6 +28,7 @@ install_requires=[ 'nansat', 'scipy', + 'beautifulsoup4', ], include_package_data=True, zip_safe=False) From c624a7eae78ce4d04adc23efbb033f4bf15452d2 Mon Sep 17 00:00:00 2001 From: akorosov Date: Tue, 12 Dec 2023 10:30:49 +0100 Subject: [PATCH 22/25] refactor s1_correction --- s1denoise/scripts/s1_correction.py | 51 +++++++++++++++++++++++++----- s1denoise/sentinel1image.py | 36 +++++++-------------- s1denoise/tools.py | 21 +++++------- 3 files changed, 62 insertions(+), 46 deletions(-) diff --git a/s1denoise/scripts/s1_correction.py b/s1denoise/scripts/s1_correction.py index 108076e..d745f9f 100755 --- a/s1denoise/scripts/s1_correction.py +++ b/s1denoise/scripts/s1_correction.py @@ -3,6 +3,12 @@ import argparse import numpy as np +try: + from nansat import Nansat +except ImportError: + NANSAT_AVAILABLE = False +else: + NANSAT_AVAILABLE = True from s1denoise.tools import run_correction @@ -10,17 +16,46 @@ def parse_args(args): ''' parse input arguments ''' parser = argparse.ArgumentParser( description="Correct Sentinel-1 TOPSAR EW GRDM for thermal and texture noise and angular dependence") - parser.add_argument('ifile', type=str, help='input Sentinel-1 file in SAFE format') - parser.add_argument('ofile', type=str, help='output GeoTIFF file') - parser.add_argument('-m', '--mask', help='Export land mask as a numpy file', action='store_true') - parser.add_argument('-a', '--algorithm', help='Name of the algorithm to use', type=str, default='NERSC') + parser.add_argument('ifile', type=str, help='Input Sentinel-1 file in SAFE or zip format') + parser.add_argument('ofile', type=str, help='Output file') + parser.add_argument('-a', '--algorithm', + help='Name of the algorithm to use', + type=str, + default='NERSC', + choices=['ESA', 'NERSC', 'NERSC_TG']) + parser.add_argument('-g', '--geotiff', help='Export resulst as a GeoTIFF file', action='store_true') + parser.add_argument('-m', '--mask', help='Also export land mask as a numpy file', action='store_true') return parser.parse_args(args) +def export_geotiff(ifile, ofile, d, mask): + """ Use Nansat to export the arrays as a geotiff file with georeference as in the input dataset """ + remove_metadata = ['dataType', 'PixelFunctionType', 'SourceBand', 'SourceFilename', 'colormap', 'minmax', 'units', 'wkv'] + n_inp = Nansat(ifile) + n_out = Nansat.from_domain(n_inp) + for pol in d: + parameters = n_inp.get_metadata(band_id='sigma0_%s' % pol) + for i in remove_metadata: + parameters.pop(i) + n_out.add_band(array=d[pol], parameters=parameters) + n_out.set_metadata(n_inp.get_metadata()) + n_out.export(ofile, driver='GTiff') + +def export_mask(ifile, ofile): + """ Export landmask as a numpy file """ + n_inp = Nansat(ifile) + wm = n_inp.watermask() + np.savez_compressed(ofile + '_mask.npz', mask=wm[1]) if __name__ == '__main__': args = parse_args(sys.argv[1:]) - s1 = run_correction(args.ifile, algorithm=args.algorithm) - s1.export(args.ofile, driver='GTiff') + if (args.geotiff or args.mask) and not NANSAT_AVAILABLE: + raise ImportError(' Nansat is not installed and I can\'t export geotif or landmask. ' + ' Install Nansat or do not use "-g" and "-m". ') + + d = run_correction(args.ifile, algorithm=args.algorithm) + if args.geotiff: + export_geotiff(args.ifile, args.ofile, d, args.mask) + else: + np.savez_compressed(args.ofile, **d) if args.mask: - wm = s1.watermask() - np.savez_compressed(args.ofile + '_mask.npz', mask=wm[1]) + export_mask(args.ifile, args.ofile) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index d528e99..b506d8f 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -662,35 +662,15 @@ def get_raw_sigma0_vectors_from_full_size(self, line, pixel, swath_ids, sigma0_f raw_sigma0[i][gpi] = s0_gpi return raw_sigma0 - def get_lons_vectors_from_full_size(self, line, pixel, lons_fs): - """ Read lons from input GeoTIff for given lines and for given pixels + def get_vectors_from_full_size(self, line, pixel, array): + """ Read array from input GeoTIff for given lines and for given pixels from full size longitude matrix """ - lons_res = [np.zeros(p.size)+np.nan for p in pixel] + array_vecs = [np.zeros(p.size)+np.nan for p in pixel] for i in range(line.shape[0]): - lons_res[i] = lons_fs[line[i]][pixel[i]] - return lons_res - - def get_lats_vectors_from_full_size(self, line, pixel, lats_fs): - """ Read lats from input GeoTIff for given lines and for given pixels - from full size latitude matrix - - """ - lats_res = [np.zeros(p.size)+np.nan for p in pixel] - for i in range(line.shape[0]): - lats_res[i] = lats_fs[line[i]][pixel[i]] - return lats_res - - def get_ia_vectors_from_full_size(self, line, pixel, ia_fs): - """ Read incidence angle values from input GeoTIff for given lines and for given pixels - from full size incidence angle matrix - - """ - ia_res = [np.zeros(p.size)+np.nan for p in pixel] - for i in range(line.shape[0]): - ia_res[i] = ia_fs[line[i]][pixel[i]] - return ia_res + array_vecs[i] = array[line[i]][pixel[i]] + return array_vecs def compute_rqm(self, s0, pol, num_px=100, **kwargs): """ Compute Range Quality Metric from the input sigma0 """ @@ -1440,3 +1420,9 @@ def get_scalloping_full_size(self, pol): z_arr = np.repeat([z_vec_fr], (lrs-frs+1), axis=0).T scall_fs[fal:lal+1, frs:lrs+1] = z_arr return scall_fs + + def get_geolocation_full_size(self, pol, name): + i = self.geolocation_interpolator(pol, self.geolocation[pol][name]) + rows = np.arange(self.shape[0]) + cols = np.arange(self.shape[1]) + return i(rows, cols) \ No newline at end of file diff --git a/s1denoise/tools.py b/s1denoise/tools.py index 5f6d0f9..6e28a32 100644 --- a/s1denoise/tools.py +++ b/s1denoise/tools.py @@ -13,8 +13,8 @@ def run_correction(ifile, angular_scale_copol=-0.2, angular_scale_crpol=-0.025, angular_offset=34.5, - output_dtype=np.float32, algorithm='NERSC', + dtype=np.float32, **kwargs): """ Run thermal, textural and angular correction of input Sentinel-1 file @@ -28,8 +28,8 @@ def run_correction(ifile, Scale for angular correction of sigma0 in HV or VH angular_offset : float Central angle for sigma0 normalization - output_dtype : dtype - Type of output array + dtype : np.dtype + Output datatype **kwargs : dict dummy keyword args for Sentinel1Image.remove_texture_noise() @@ -47,18 +47,13 @@ def run_correction(ifile, } s1 = Sentinel1Image(ifile) - n = Nansat.from_domain(s1) - inc = s1['incidence_angle'] + d = {} for pol in s1.pols: print('Correct %s band' % pol) - parameters = s1.get_metadata(band_id='sigma0_%s' % pol) - for i in ['dataType', 'PixelFunctionType', 'SourceBand', 'SourceFilename']: - parameters.pop(i) - array = s1.remove_texture_noise(pol, algorithm=algorithm, **kwargs) - array = 10 * np.log10(array) - scale[pol] * (inc - angular_offset) - n.add_band(array=array.astype(output_dtype), parameters=parameters) - n.set_metadata(s1.get_metadata()) - return n + inc = s1.get_geolocation_full_size(pol, 'incidenceAngle') + d[pol] = s1.remove_texture_noise(pol, algorithm=algorithm, **kwargs) + d[pol] = (10 * np.log10(d[pol]) - scale[pol] * (inc - angular_offset)).astype(dtype) + return d class AnalyzeExperiment(): From c70629097ca11cfbf4250c301058d5a13fefbb6d Mon Sep 17 00:00:00 2001 From: akorosov Date: Tue, 12 Dec 2023 12:45:46 +0100 Subject: [PATCH 23/25] refactor lru_cache --- s1denoise/sentinel1image.py | 241 ++++++++++++++++-------------------- setup.py | 12 +- 2 files changed, 116 insertions(+), 137 deletions(-) diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index b506d8f..7ee0bd4 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -8,7 +8,7 @@ import shutil import xml.etree.ElementTree as ET import zipfile -from functools import cached_property, cache +from functools import lru_cache import numpy as np from osgeo import gdal @@ -46,36 +46,37 @@ def __init__(self, s1): manifest_file = [f for f in s1.filenames if 'manifest.safe' in f][0] # find annotation, calibration and noise files - self.xml = dict(annotation = {}, calibration = {}, noise = {}) + xml_names = ['annotation', 'calibration', 'noise'] + self.__dict__.update({name : {} for name in xml_names}) for pol in [self.txPol + 'H', self.txPol + 'V']: - self.xml['annotation'][pol] = [f for f in s1.filenames if ('annotation/s1' in f and pol.lower() in f)][0] - self.xml['calibration'][pol] = [f for f in s1.filenames if ('calibration-s1' in f and pol.lower() in f)][0] - self.xml['noise'][pol] = [f for f in s1.filenames if ('noise-s1' in f and pol.lower() in f)][0] + self.annotation[pol] = [f for f in s1.filenames if ('annotation/s1' in f and pol.lower() in f)][0] + self.calibration[pol] = [f for f in s1.filenames if ('calibration-s1' in f and pol.lower() in f)][0] + self.noise[pol] = [f for f in s1.filenames if ('noise-s1' in f and pol.lower() in f)][0] # read and parse XML files if zipfile.is_zipfile(s1.filename): with zipfile.PyZipFile(s1.filename) as zf: - for name in self.xml: - for pol in self.xml[name]: - self.xml[name][pol] = BeautifulSoup(zf.read(self.xml[name][pol]), features="xml") - self.xml['manifest'] = BeautifulSoup(zf.read(manifest_file), features="xml") + for name in xml_names: + for pol in self.__dict__[name]: + self.__dict__[name][pol] = BeautifulSoup(zf.read(self.__dict__[name][pol]), features="xml") + self.manifest = BeautifulSoup(zf.read(manifest_file), features="xml") else: - for name in self.xml: - for pol in self.xml[name]: - with open(self.xml[name][pol]) as ff: - self.xml[name][pol] = BeautifulSoup(ff.read(), features="xml") + for name in xml_names: + for pol in self.__dict__[name]: + with open(self.__dict__[name][pol]) as ff: + self.__dict__[name][pol] = BeautifulSoup(ff.read(), features="xml") with open(manifest_file) as ff: - self.xml['manifest'] = BeautifulSoup(ff.read(), features="xml") + self.manifest = BeautifulSoup(ff.read(), features="xml") # get the auxiliary calibration file - for resource in (self.xml['manifest'].find_all('resource') + - self.xml['manifest'].find_all('safe:resource')): + for resource in (self.manifest.find_all('resource') + + self.manifest.find_all('safe:resource')): if resource.attrs['role'] == 'AUX_CAL': auxCalibFilename = resource.attrs['name'].split('/')[-1] self.set_aux_data_dir() self.download_aux_calibration(auxCalibFilename) with open(self.auxiliaryCalibration_file) as f: - self.xml['auxiliary'] = BeautifulSoup(f.read(), features="xml") + self.auxiliary = BeautifulSoup(f.read(), features="xml") def set_aux_data_dir(self): """ Set directory where aux calibration data is stored """ @@ -117,22 +118,6 @@ def get_remote_url(api_url): # the best found AUX-CAL file is copied to the filename from manifest shutil.copytree(download_file.rstrip('.zip'), output_dir) - @property - def annotation(self): - return self.xml['annotation'] - @property - def calibration(self): - return self.xml['calibration'] - @property - def noise(self): - return self.xml['noise'] - @property - def manifest(self): - return self.xml['manifest'] - @property - def auxiliary(self): - return self.xml['auxiliary'] - class Sentinel1Image(): """ Thermal noise correction for S1 GRD data""" @@ -180,21 +165,21 @@ def find_filesnames(self): else: self.filenames = [str(i) for i in Path(f'{self.filename}/').rglob('*')] - @cached_property + @property def time_coverage_start(self): return datetime.strptime(os.path.basename(self.filename).split('_')[4], '%Y%m%dT%H%M%S') - @cached_property + @property def time_coverage_end(self): return datetime.strptime(os.path.basename(self.filename).split('_')[5], '%Y%m%dT%H%M%S') - @cached_property - def shape(self): - a = self.xml.annotation[self.pols[0]] + @lru_cache + def shape(self, pol): + a = self.xml.annotation[pol] return [int(a.find(i).text) for i in ['numberOfLines', 'numberOfSamples']] - @cached_property - def swath_bounds(self): + @lru_cache + def swath_bounds(self, pol): """ Boundaries of blocks in each swath for each polarisation """ names = { 'azimuthTime' : parse_azimuth_time, @@ -204,17 +189,15 @@ def swath_bounds(self): 'lastRangeSample' : int, } swath_bounds = {} - for pol in self.pols: - swath_bounds[pol] = {} - for swathMerge in self.xml.annotation[pol].find_all('swathMerge'): - swath_bounds[pol][swathMerge.swath.text] = defaultdict(list) - for swathBounds in swathMerge.find_all('swathBounds'): - for name in names: - swath_bounds[pol][swathMerge.swath.text][name].append(names[name](swathBounds.find(name).text)) + for swathMerge in self.xml.annotation[pol].find_all('swathMerge'): + swath_bounds[swathMerge.swath.text] = defaultdict(list) + for swathBounds in swathMerge.find_all('swathBounds'): + for name in names: + swath_bounds[swathMerge.swath.text][name].append(names[name](swathBounds.find(name).text)) return swath_bounds - @cached_property - def geolocation(self): + @lru_cache + def geolocation(self, pol): ''' Import geolocationGridPoint from annotation XML DOM ''' geolocation_keys = { 'azimuthTime': str, #lambda x : datetime.strptime(x, '%Y-%m-%dT%H:%M:%S.%f'), @@ -227,49 +210,43 @@ def geolocation(self): 'incidenceAngle':float, 'elevationAngle':float, } - geolocation = {} - for pol in self.pols: - geolocation[pol] = defaultdict(list) - for p in self.xml.annotation[pol].find_all('geolocationGridPoint'): - for c in p: - if c.name: - geolocation[pol][c.name].append(geolocation_keys[c.name](c.text)) - geolocation[pol]['line'] = np.unique(geolocation[pol]['line']) - geolocation[pol]['pixel'] = np.unique(geolocation[pol]['pixel']) - for i in geolocation[pol]: - if i not in ['line', 'pixel']: - geolocation[pol][i] = np.array(geolocation[pol][i]).reshape( - geolocation[pol]['line'].size, - geolocation[pol]['pixel'].size - ) + geolocation = defaultdict(list) + for p in self.xml.annotation[pol].find_all('geolocationGridPoint'): + for c in p: + if c.name: + geolocation[c.name].append(geolocation_keys[c.name](c.text)) + geolocation['line'] = np.unique(geolocation['line']) + geolocation['pixel'] = np.unique(geolocation['pixel']) + for i in geolocation: + if i not in ['line', 'pixel']: + geolocation[i] = np.array(geolocation[i]).reshape( + geolocation['line'].size, + geolocation['pixel'].size + ) return geolocation - @cached_property - def geolocation_relative_azimuth_time(self): - az_time = {} - for pol in self.pols: - az_time[pol] = list(map(parse_azimuth_time, self.geolocation[pol]['azimuthTime'].flat)) - az_time[pol] = [ (t-self.time_coverage_center).total_seconds() for t in az_time[pol]] - az_time[pol] = np.array(az_time[pol]).reshape(self.geolocation[pol]['azimuthTime'].shape) + @lru_cache + def geolocation_relative_azimuth_time(self, pol): + az_time = list(map(parse_azimuth_time, self.geolocation(pol)['azimuthTime'].flat)) + az_time = [ (t-self.time_coverage_center).total_seconds() for t in az_time] + az_time = np.array(az_time).reshape(self.geolocation(pol)['azimuthTime'].shape) return az_time - @cached_property - def calibration(self): - calibration = {} - for pol in self.pols: - calibration[pol] = defaultdict(list) - for cv in self.xml.calibration[pol].find_all('calibrationVector'): - for n in cv: - if n.name: - calibration[pol][n.name].append(n.text) - calibration[pol]['azimuthTime'] = list(map(parse_azimuth_time, calibration[pol]['azimuthTime'])) - calibration[pol]['line'] = np.array(list(map(int, calibration[pol]['line']))) - calibration[pol]['pixel'] = np.array([list(map(int, p.split())) for p in calibration[pol]['pixel']]) - for key in ['sigmaNought', 'betaNought', 'gamma', 'dn']: - calibration[pol][key] = np.array([list(map(float, p.split())) for p in calibration[pol][key]]) + @lru_cache + def calibration(self, pol): + calibration = defaultdict(list) + for cv in self.xml.calibration[pol].find_all('calibrationVector'): + for n in cv: + if n.name: + calibration[n.name].append(n.text) + calibration['azimuthTime'] = list(map(parse_azimuth_time, calibration['azimuthTime'])) + calibration['line'] = np.array(list(map(int, calibration['line']))) + calibration['pixel'] = np.array([list(map(int, p.split())) for p in calibration['pixel']]) + for key in ['sigmaNought', 'betaNought', 'gamma', 'dn']: + calibration[key] = np.array([list(map(float, p.split())) for p in calibration[key]]) return calibration - @cached_property + @lru_cache def aux_calibration_params(self): swaths = [f'{self.obsMode}{li}' for li in self.swath_ids] calibration_params = { @@ -289,7 +266,7 @@ def aux_calibration_params(self): calibration_params[pol][swath]['azimuthAntennaPattern'] = np.array([float(i) for i in calibrationParams.azimuthAntennaElementPattern.values.text.split()]) return calibration_params - @cache + @lru_cache def noise_range(self, pol): if self.IPFversion < 2.9: noiseRangeVectorName = 'noiseVector' @@ -306,7 +283,7 @@ def noise_range(self, pol): noise_range['line'] = np.array(noise_range['line']) return noise_range - @cache + @lru_cache def noise_azimuth(self, pol): noise_azimuth = {f'{self.obsMode}{swid}':defaultdict(list) for swid in self.swath_ids} if self.IPFversion < 2.9: @@ -314,9 +291,9 @@ def noise_azimuth(self, pol): noise_azimuth[f'{self.obsMode}{swid}'] = dict( firstAzimuthLine = [0], firstRangeSample = [0], - lastAzimuthLine = [self.shape[0]-1], - lastRangeSample = [self.shape[1]-1], - line = [np.array([0, self.shape[0]-1])], + lastAzimuthLine = [self.shape(pol)[0]-1], + lastRangeSample = [self.shape(pol)[1]-1], + line = [np.array([0, self.shape(pol)[0]-1])], noise = [np.array([1.0, 1.0])]) else: int_names = ['firstAzimuthLine', 'firstRangeSample', 'lastAzimuthLine', 'lastRangeSample'] @@ -332,7 +309,7 @@ def get_swath_id_vectors(self, pol, pixel=None): if pixel is None: pixel = self.noise_range(pol)['pixel'] swath_indices = [np.zeros(p.shape, int) for p in pixel] - swathBounds = self.swath_bounds[pol] + swathBounds = self.swath_bounds(pol) for iswath, swath_name in enumerate(swathBounds): swathBound = swathBounds[swath_name] @@ -419,7 +396,7 @@ def get_tg_scales_offsets(self): def get_swath_interpolator(self, pol, swath_name, line, pixel, z): """ Prepare interpolators for one swath """ - swathBound = self.swath_bounds[pol][swath_name] + swathBound = self.swath_bounds(pol)[swath_name] swath_coords = ( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -453,12 +430,12 @@ def get_swath_interpolator(self, pol, swath_name, line, pixel, z): def geolocation_interpolator(self, pol, z): return RectBivariateSpline( - self.geolocation[pol]['line'], - self.geolocation[pol]['pixel'], + self.geolocation(pol)['line'], + self.geolocation(pol)['pixel'], z) def get_angle_vectors(self, pol, angle_name): - z = self.geolocation[pol][angle_name] + z = self.geolocation(pol)[angle_name] i = self.geolocation_interpolator(pol, z) angle_vectors = [i(l, p).flatten() for (l, p) in zip(line, pixel)] return angle_vectors @@ -467,9 +444,9 @@ def get_calibration_vectors(self, pol, name='sigmaNought'): line = self.noise_range(pol)['line'] pixel = self.noise_range(pol)['pixel'] swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] - s0line = self.calibration[pol]['line'] - s0pixel = self.calibration[pol]['pixel'] - sigma0 = self.calibration[pol][name] + s0line = self.calibration(pol)['line'] + s0pixel = self.calibration(pol)['pixel'] + sigma0 = self.calibration(pol)[name] sigma0_vecs = [np.zeros_like(p_vec) + np.nan for p_vec in pixel] @@ -497,9 +474,9 @@ def get_eap_interpolator(self, subswathID, pol): It computes EAP for input boresight angles """ - eap_lut = self.aux_calibration_params[pol][subswathID]['elevationAntennaPattern'] - recordLength = self.aux_calibration_params[pol][subswathID]['elevationAntennaPatternCount'] - eai_lut = self.aux_calibration_params[pol][subswathID]['elevationAngleIncrement'] + eap_lut = self.aux_calibration_params()[pol][subswathID]['elevationAntennaPattern'] + recordLength = self.aux_calibration_params()[pol][subswathID]['elevationAntennaPatternCount'] + eai_lut = self.aux_calibration_params()[pol][subswathID]['elevationAngleIncrement'] if recordLength == eap_lut.shape[0]: # in case if elevationAntennaPattern is given in dB amplitudeLUT = 10**(eap_lut/10) @@ -528,9 +505,9 @@ def get_boresight_angle_interpolator(self, pol): relativeAzimuthTime = np.hstack(relativeAzimuthTime) rollAngle = np.hstack(rollAngle) rollAngleIntp = InterpolatedUnivariateSpline(relativeAzimuthTime[sortIndex], rollAngle[sortIndex]) - roll_map = rollAngleIntp(self.geolocation_relative_azimuth_time[pol]) + roll_map = rollAngleIntp(self.geolocation_relative_azimuth_time(pol)) - boresight_map = self.geolocation[pol]['elevationAngle'] - roll_map + boresight_map = self.geolocation(pol)['elevationAngle'] - roll_map boresight_angle_interpolator = self.geolocation_interpolator(pol, boresight_map) return boresight_angle_interpolator @@ -540,7 +517,7 @@ def get_range_spread_loss_interpolator(self, pol, rsl_power=3./2.): """ referenceRange = float(self.xml.annotation[pol].find('referenceRange').text) - rangeSpreadingLoss = (referenceRange / self.geolocation[pol]['slantRangeTime'] / SPEED_OF_LIGHT * 2)**rsl_power + rangeSpreadingLoss = (referenceRange / self.geolocation(pol)['slantRangeTime'] / SPEED_OF_LIGHT * 2)**rsl_power rsp_interpolator = self.geolocation_interpolator(pol, rangeSpreadingLoss) return rsp_interpolator @@ -558,7 +535,7 @@ def get_shifted_noise_vectors(self, pol, pixel=None, noise=None, skip=4, min_val # noise lut shift for swid in self.swath_ids: swath_name = f'{self.obsMode}{swid}' - swathBound = self.swath_bounds[pol][swath_name] + swathBound = self.swath_bounds(pol)[swath_name] eap_interpolator = self.get_eap_interpolator(swath_name, pol) ba_interpolator = self.get_boresight_angle_interpolator(pol) rsp_interpolator = self.get_range_spread_loss_interpolator(pol, rsl_power=rsl_power) @@ -606,7 +583,7 @@ def get_corrected_noise_vectors(self, pol, nesz, pixel=None, add_pb=True): ns, pb = self.import_denoisingCoefficients(pol)[:2] for swid in self.swath_ids: swath_name = f'{self.obsMode}{swid}' - swathBound = self.swath_bounds[pol][swath_name] + swathBound = self.swath_bounds(pol)[swath_name] zipped = zip( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -680,7 +657,7 @@ def compute_rqm(self, s0, pol, num_px=100, **kwargs): for swid in self.swath_ids[:-1]: q_subswath = [] swath_name = f'{self.obsMode}{swid}' - swathBound = self.swath_bounds[pol][swath_name] + swathBound = self.swath_bounds(pol)[swath_name] zipped = zip( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -753,7 +730,7 @@ def experiment_get_data(self, pol, average_lines, zoom_step): line = self.noise_range(pol)['line'] sigma0 = self.get_raw_sigma0_vectors_from_full_size( pol, line, pixel, sigma0_fs, average_lines=average_lines) - return line, pixel, sigma0, nesz, crop, self.swath_bounds[pol] + return line, pixel, sigma0, nesz, crop, self.swath_bounds(pol) def experiment_noiseScaling(self, pol, average_lines=777, zoom_step=2): """ Compute noise scaling coefficients for each range noise line and save as NPZ """ @@ -910,7 +887,7 @@ def experiment_powerBalancing(self, pol, average_lines=777, zoom_step=2): def interp_nrv_full_size(self, z, line, pixel, pol, power=1): """ Interpolate noise range vectors to full size """ - z_fs = np.zeros(self.shape) + np.nan + z_fs = np.zeros(self.shape(pol)) + np.nan swath_names = [f'{self.obsMode}{i}' for i in self.swath_ids] for swath_name in swath_names: z_interp2, swath_coords = self.get_swath_interpolator(pol, swath_name, line, pixel, z) @@ -932,7 +909,7 @@ def get_corrected_nesz_full_size(self, pol, nesz): # skip correction id NS/PB coeffs are not available (e.g. HH or VV) if swath_name not in ns: continue - swathBound = self.swath_bounds[pol][swath_name] + swathBound = self.swath_bounds(pol)[swath_name] zipped = zip( swathBound['firstAzimuthLine'], swathBound['lastAzimuthLine'], @@ -951,9 +928,9 @@ def get_raw_sigma0_full_size(self, pol, min_dn=0): dn = ds.ReadAsArray() sigma0_fs = dn.astype(float)**2 / self.interp_nrv_full_size( - self.calibration[pol]['sigmaNought'], - self.calibration[pol]['line'], - self.calibration[pol]['pixel'], + self.calibration(pol)['sigmaNought'], + self.calibration(pol)['line'], + self.calibration(pol)['pixel'], pol, power=2) sigma0_fs[dn <= min_dn] = np.nan return sigma0_fs @@ -1020,7 +997,7 @@ def remove_thermal_noise(self, pol, algorithm='NERSC', remove_negative=True, min sigma0 = fill_gaps(sigma0, sigma0 <= 0) return sigma0 - @cache + @lru_cache def antenna_pattern(self, pol): list_keys = ['slantRangeTime', 'elevationAngle', 'elevationPattern', 'incidenceAngle'] antenna_pattern = {} @@ -1042,7 +1019,7 @@ def antenna_pattern(self, pol): antenna_pattern[swath]['roll'] = self.compute_roll(pol, antenna_pattern[swath]) return antenna_pattern - @cache + @lru_cache def import_orbit(self, pol): ''' Import orbit information from annotation XML DOM ''' orbit = { 'time':[], @@ -1204,9 +1181,9 @@ def remove_texture_noise(self, pol, window=3, weight=0.1, s0_min=0, remove_negat def subswathIndexMap(self, pol): ''' Convert subswath indices into full grid pixels ''' - subswathIndexMap = np.zeros(self.shape, dtype=np.uint8) + subswathIndexMap = np.zeros(self.shape(pol), dtype=np.uint8) for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): - swathBound = self.swath_bounds[pol]['%s%s' % (self.obsMode, iSW)] + swathBound = self.swath_bounds(pol)['%s%s' % (self.obsMode, iSW)] zipped = zip(swathBound['firstAzimuthLine'], swathBound['firstRangeSample'], swathBound['lastAzimuthLine'], @@ -1217,7 +1194,7 @@ def subswathIndexMap(self, pol): def subswathCenterSampleIndex(self, pol): ''' Range center pixel indices along azimuth for each subswath ''' - swathBounds = self.swath_bounds[pol] + swathBounds = self.swath_bounds(pol) subswathCenterSampleIndex = {} for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): subswathID = '%s%s' % (self.obsMode, iSW) @@ -1264,7 +1241,7 @@ def import_azimuthFmRate(self, pol): azimuthFmRate['azimuthFmRatePolynomial'].append(afmrp) return azimuthFmRate - @cache + @lru_cache def focusedBurstLengthInTime(self, pol): ''' Get focused burst length in zero-Doppler time domain @@ -1291,20 +1268,20 @@ def focusedBurstLengthInTime(self, pol): raise ValueError('number of bursts cannot be determined.') return focusedBurstLengthInTime - @cache + @lru_cache def scalloping_gain(self, pol, subswathID): # azimuth antenna element patterns (AAEP) lookup table for given subswath - gainAAEP = self.aux_calibration_params[pol][subswathID]['azimuthAntennaPattern'] - azimuthAngleIncrement = self.aux_calibration_params[pol][subswathID]['azimuthAngleIncrement'] + gainAAEP = self.aux_calibration_params()[pol][subswathID]['azimuthAntennaPattern'] + azimuthAngleIncrement = self.aux_calibration_params()[pol][subswathID]['azimuthAngleIncrement'] angleAAEP = np.arange(-(len(gainAAEP)//2), len(gainAAEP)//2+1) * azimuthAngleIncrement # subswath range center pixel index subswathCenterSampleIndex = self.subswathCenterSampleIndex(pol)[subswathID] # slant range time along subswath range center - interpolator = self.geolocation_interpolator(pol, self.geolocation[pol]['slantRangeTime']) - slantRangeTime = np.squeeze(interpolator(np.arange(self.shape[0]), subswathCenterSampleIndex)) + interpolator = self.geolocation_interpolator(pol, self.geolocation(pol)['slantRangeTime']) + slantRangeTime = np.squeeze(interpolator(np.arange(self.shape(pol)[0]), subswathCenterSampleIndex)) # relative azimuth time along subswath range center - interpolator = self.geolocation_interpolator(pol, self.geolocation_relative_azimuth_time[pol]) - azimuthTime = np.squeeze(interpolator(np.arange(self.shape[0]), subswathCenterSampleIndex)) + interpolator = self.geolocation_interpolator(pol, self.geolocation_relative_azimuth_time(pol)) + azimuthTime = np.squeeze(interpolator(np.arange(self.shape(pol)[0]), subswathCenterSampleIndex)) # Doppler rate induced by satellite motion motionDopplerRate = self.azimuthFmRateAtGivenTime(pol, azimuthTime, slantRangeTime) # antenna steering rate @@ -1386,7 +1363,7 @@ def get_noise_azimuth_vectors(self, pol, pixel=None): def get_scalloping_full_size(self, pol): """ Interpolate noise azimuth vector to full resolution for all blocks """ - scall_fs = np.zeros(self.shape) + scall_fs = np.zeros(self.shape(pol)) if self.IPFversion < 2.9: subswathIndexMap = self.subswathIndexMap(pol) for iSW in range(1, {'IW':3, 'EW':5}[self.obsMode]+1): @@ -1395,7 +1372,7 @@ def get_scalloping_full_size(self, pol): # assign computed scalloping gain into each subswath valid = (subswathIndexMap==iSW) scall_fs[valid] = ( - scallopingGain[:, np.newaxis] * np.ones((1,self.shape[1])))[valid] + scallopingGain[:, np.newaxis] * np.ones((1,self.shape(pol)[1])))[valid] return scall_fs noiseAzimuthVector = self.noise_azimuth(pol) @@ -1422,7 +1399,7 @@ def get_scalloping_full_size(self, pol): return scall_fs def get_geolocation_full_size(self, pol, name): - i = self.geolocation_interpolator(pol, self.geolocation[pol][name]) - rows = np.arange(self.shape[0]) - cols = np.arange(self.shape[1]) + i = self.geolocation_interpolator(pol, self.geolocation(pol)[name]) + rows = np.arange(self.shape(pol)[0]) + cols = np.arange(self.shape(pol)[1]) return i(rows, cols) \ No newline at end of file diff --git a/setup.py b/setup.py index b1e5908..5347535 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name='s1denoise', - version='1.3.2', + version='1.4.1', description='Thermal noise correction of Sentinel-1 TOPS GRDM EW products ', classifiers=[ 'Development Status :: 5 - Production/Stable', @@ -11,8 +11,7 @@ 'Intended Audience :: Science/Research', 'Natural Language :: English', 'Operating System :: OS Independent', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3', 'Topic :: Scientific/Engineering', 'Topic :: Scientific/Engineering :: Information Analysis', 'Topic :: Utilities' @@ -26,9 +25,12 @@ ], package_data={'s1denoise' :['denoising_parameters.json']}, install_requires=[ - 'nansat', - 'scipy', 'beautifulsoup4', + 'gdal', + 'lxml', + 'numpy', + 'requests', + 'scipy', ], include_package_data=True, zip_safe=False) From 4ea04a4278c081f15eff84fa0e5bd3ab4f190278 Mon Sep 17 00:00:00 2001 From: akorosov Date: Tue, 12 Dec 2023 13:08:49 +0100 Subject: [PATCH 24/25] refactor lru_cache, add envitonment, update readme --- README.md | 32 ++++++++++++++++---------------- environment.yml | 9 +++++++++ s1denoise/sentinel1image.py | 24 ++++++++++++------------ setup.py | 2 +- 4 files changed, 38 insertions(+), 29 deletions(-) create mode 100644 environment.yml diff --git a/README.md b/README.md index af76f5d..a69f0ac 100644 --- a/README.md +++ b/README.md @@ -22,19 +22,13 @@ is to use [Anaconda](https://docs.conda.io/en/latest/miniconda.html). ```shell # create conda environment with key requirements -conda create -y -n s1denoise gdal cartopy pip +conda env create -f environment.yml # activate environment conda activate s1denoise -# install other reqs using pip -pip install pythesint netcdf4 nansat - -# update metadata vocabularies -python -c 'import pythesint as pti; pti.update_all_vocabularies()' - # install s1denoise -pip install https://github.com/nansencenter/sentinel1denoised/archive/v1.3.3.tar.gz +pip install https://github.com/nansencenter/sentinel1denoised/archive/v1.4.0.tar.gz ``` @@ -60,25 +54,32 @@ s1 = Sentinel1Image('/path/to/data/S1B_EW_GRDM_1SDH_INPUTFILE.zip') # run thermal noise correction in HV polarisation with the default ESA algorithm s0hve = s1.remove_thermal_noise('HV', algorithm='ESA') -# run thermal noise correction in HV polarisation with the NEW algorithm +# run thermal noise correction in HV polarisation with the default NERSC algorithm s0_hv = s1.remove_thermal_noise('HV') +# run thermal noise correction in HV polarisation with the NERSC_TG algorithm applicable for old Sentinel-1 data +s0_hv = s1.remove_thermal_noise('HV', algorithm='NERSC_TG') + # run thermal and texture noise correction in HV polarisation s0_hv = s1.remove_texture_noise('HV') ``` -Process a single file with thermal, textural and angular correction and export in dB: +Process a single file with thermal, textural and angular correction and export in [dB] in a numpy file: -`s1_correction.py INPUTFILE.zip OUTPUTFILE.tif` +`s1_correction.py INPUTFILE.zip OUTPUTFILE.npz` -Process a single file using Docker (replace `input_dir` and `output_dir` with actual directories): +Process a single file and export as GeoTIFF (requires Nansat): -`docker run --rm -v /input_dir:/input_dir -v /output_dir:/output_dir s1denoise s1_correction.py /input_dir/INPUTFILE.zip /output_dir/OUPUTFILE.tif` +`s1_correction.py INPUTFILE.zip OUTPUTFILE.npz -g` With option `-m` the script will also save landmask in Numpy format in a separate file with name `OUTPUTFILE.tif_mask.npz`: -`s1_correction.py -m INPUTFILE.zip OUTPUTFILE.tif` +`s1_correction.py INPUTFILE.zip OUTPUTFILE.tif -m` + +Process a single file using Docker (replace `input_dir` and `output_dir` with actual directories): + +`docker run --rm -v /data_dir:/data_dir s1denoise s1_correction.py /data_dir/INPUTFILE.zip /data_dir/OUPUTFILE.tif` Note that for enabling the landmask functionality, you need to download and install the watermask: @@ -95,8 +96,7 @@ rm $MOD44WPATH/MOD44W.tgz ## Experimental scripts -Sub-directories in `s1denoise/experimentalData` contain scripts for training the noise scaling and power balancing coefficients and extra scaling. -See README files in these sub-dirs for details. +Sub-directories in `s1denoise/training` and `s1denoise/validation` contain scripts for training and validation of the noise scaling and power balancing coefficients and extra scaling. See README files in these sub-dirs for details. ## License The project is licensed under the GNU general public license version 3. diff --git a/environment.yml b/environment.yml new file mode 100644 index 0000000..01b0a9f --- /dev/null +++ b/environment.yml @@ -0,0 +1,9 @@ +name: s1denoise +dependencies: + - gdal + - lxml + - pip + - pip: + - beautifulsoup4 + - requests + diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index 7ee0bd4..81e60c9 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -173,12 +173,12 @@ def time_coverage_start(self): def time_coverage_end(self): return datetime.strptime(os.path.basename(self.filename).split('_')[5], '%Y%m%dT%H%M%S') - @lru_cache + @lru_cache(maxsize=None) def shape(self, pol): a = self.xml.annotation[pol] return [int(a.find(i).text) for i in ['numberOfLines', 'numberOfSamples']] - @lru_cache + @lru_cache(maxsize=None) def swath_bounds(self, pol): """ Boundaries of blocks in each swath for each polarisation """ names = { @@ -196,7 +196,7 @@ def swath_bounds(self, pol): swath_bounds[swathMerge.swath.text][name].append(names[name](swathBounds.find(name).text)) return swath_bounds - @lru_cache + @lru_cache(maxsize=None) def geolocation(self, pol): ''' Import geolocationGridPoint from annotation XML DOM ''' geolocation_keys = { @@ -225,14 +225,14 @@ def geolocation(self, pol): ) return geolocation - @lru_cache + @lru_cache(maxsize=None) def geolocation_relative_azimuth_time(self, pol): az_time = list(map(parse_azimuth_time, self.geolocation(pol)['azimuthTime'].flat)) az_time = [ (t-self.time_coverage_center).total_seconds() for t in az_time] az_time = np.array(az_time).reshape(self.geolocation(pol)['azimuthTime'].shape) return az_time - @lru_cache + @lru_cache(maxsize=None) def calibration(self, pol): calibration = defaultdict(list) for cv in self.xml.calibration[pol].find_all('calibrationVector'): @@ -246,7 +246,7 @@ def calibration(self, pol): calibration[key] = np.array([list(map(float, p.split())) for p in calibration[key]]) return calibration - @lru_cache + @lru_cache(maxsize=None) def aux_calibration_params(self): swaths = [f'{self.obsMode}{li}' for li in self.swath_ids] calibration_params = { @@ -266,7 +266,7 @@ def aux_calibration_params(self): calibration_params[pol][swath]['azimuthAntennaPattern'] = np.array([float(i) for i in calibrationParams.azimuthAntennaElementPattern.values.text.split()]) return calibration_params - @lru_cache + @lru_cache(maxsize=None) def noise_range(self, pol): if self.IPFversion < 2.9: noiseRangeVectorName = 'noiseVector' @@ -283,7 +283,7 @@ def noise_range(self, pol): noise_range['line'] = np.array(noise_range['line']) return noise_range - @lru_cache + @lru_cache(maxsize=None) def noise_azimuth(self, pol): noise_azimuth = {f'{self.obsMode}{swid}':defaultdict(list) for swid in self.swath_ids} if self.IPFversion < 2.9: @@ -997,7 +997,7 @@ def remove_thermal_noise(self, pol, algorithm='NERSC', remove_negative=True, min sigma0 = fill_gaps(sigma0, sigma0 <= 0) return sigma0 - @lru_cache + @lru_cache(maxsize=None) def antenna_pattern(self, pol): list_keys = ['slantRangeTime', 'elevationAngle', 'elevationPattern', 'incidenceAngle'] antenna_pattern = {} @@ -1019,7 +1019,7 @@ def antenna_pattern(self, pol): antenna_pattern[swath]['roll'] = self.compute_roll(pol, antenna_pattern[swath]) return antenna_pattern - @lru_cache + @lru_cache(maxsize=None) def import_orbit(self, pol): ''' Import orbit information from annotation XML DOM ''' orbit = { 'time':[], @@ -1241,7 +1241,7 @@ def import_azimuthFmRate(self, pol): azimuthFmRate['azimuthFmRatePolynomial'].append(afmrp) return azimuthFmRate - @lru_cache + @lru_cache(maxsize=None) def focusedBurstLengthInTime(self, pol): ''' Get focused burst length in zero-Doppler time domain @@ -1268,7 +1268,7 @@ def focusedBurstLengthInTime(self, pol): raise ValueError('number of bursts cannot be determined.') return focusedBurstLengthInTime - @lru_cache + @lru_cache(maxsize=None) def scalloping_gain(self, pol, subswathID): # azimuth antenna element patterns (AAEP) lookup table for given subswath gainAAEP = self.aux_calibration_params()[pol][subswathID]['azimuthAntennaPattern'] diff --git a/setup.py b/setup.py index 5347535..b8e2bd2 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name='s1denoise', - version='1.4.1', + version='1.4.0', description='Thermal noise correction of Sentinel-1 TOPS GRDM EW products ', classifiers=[ 'Development Status :: 5 - Production/Stable', From 34ce58739ca4400a6eadc57b37f0420441c637c6 Mon Sep 17 00:00:00 2001 From: akorosov Date: Tue, 12 Dec 2023 13:27:13 +0100 Subject: [PATCH 25/25] docstrings --- README.md | 7 +++++- s1denoise/sentinel1image.py | 50 +++++++++++++++++++++++++++++++------ s1denoise/tools.py | 4 +-- 3 files changed, 50 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index a69f0ac..373c1b5 100644 --- a/README.md +++ b/README.md @@ -49,7 +49,8 @@ Do processing inside Python environment: ```python from s1denoise import Sentinel1Image # open access to file with S1 data -s1 = Sentinel1Image('/path/to/data/S1B_EW_GRDM_1SDH_INPUTFILE.zip') +input_file = '/path/to/data/S1B_EW_GRDM_1SDH_INPUTFILE.zip' +s1 = Sentinel1Image(input_file) # run thermal noise correction in HV polarisation with the default ESA algorithm s0hve = s1.remove_thermal_noise('HV', algorithm='ESA') @@ -63,6 +64,10 @@ s0_hv = s1.remove_thermal_noise('HV', algorithm='NERSC_TG') # run thermal and texture noise correction in HV polarisation s0_hv = s1.remove_texture_noise('HV') +# High level function for processing both polarisations +from s1denoise.tools import run_correction +d = run_correction(input_file) + ``` Process a single file with thermal, textural and angular correction and export in [dB] in a numpy file: diff --git a/s1denoise/sentinel1image.py b/s1denoise/sentinel1image.py index 81e60c9..e11e71f 100644 --- a/s1denoise/sentinel1image.py +++ b/s1denoise/sentinel1image.py @@ -86,6 +86,7 @@ def set_aux_data_dir(self): os.makedirs(self.aux_data_dir) def download_aux_calibration(self, filename): + """ Download AUX calibration file from APC """ self.auxiliaryCalibration_file = os.path.join(self.aux_data_dir, filename, 'data', '%s-aux-cal.xml' % self.platform.lower()) if os.path.exists(self.auxiliaryCalibration_file): return @@ -120,7 +121,18 @@ def get_remote_url(api_url): class Sentinel1Image(): - """ Thermal noise correction for S1 GRD data""" + """ Thermal noise correction for S1 GRD data + + Input + ----- + filename : str + filename of the Sentinel-1 GRD file (SAFE or ZIP format) + + Returns + ------- + s1 : Sentinel1Image + Object to perform noise correction + """ def __init__(self, filename): self.filename = filename self.find_filesnames() @@ -157,6 +169,7 @@ def __init__(self, filename): 'ESA default noise correction result might be wrong.\n') def find_filesnames(self): + """ Find all filenames in subdirecotries """ self.is_zipfile = False if zipfile.is_zipfile(self.filename): self.is_zipfile = True @@ -175,6 +188,7 @@ def time_coverage_end(self): @lru_cache(maxsize=None) def shape(self, pol): + """ Shape of the raster for input polarization """ a = self.xml.annotation[pol] return [int(a.find(i).text) for i in ['numberOfLines', 'numberOfSamples']] @@ -198,7 +212,7 @@ def swath_bounds(self, pol): @lru_cache(maxsize=None) def geolocation(self, pol): - ''' Import geolocationGridPoint from annotation XML DOM ''' + ''' Import geolocationGridPoint from annotation XML ''' geolocation_keys = { 'azimuthTime': str, #lambda x : datetime.strptime(x, '%Y-%m-%dT%H:%M:%S.%f'), 'slantRangeTime':float, @@ -227,6 +241,7 @@ def geolocation(self, pol): @lru_cache(maxsize=None) def geolocation_relative_azimuth_time(self, pol): + """ Matrix with relative azimuth time (AT) computed as second difference of AT from central AT """ az_time = list(map(parse_azimuth_time, self.geolocation(pol)['azimuthTime'].flat)) az_time = [ (t-self.time_coverage_center).total_seconds() for t in az_time] az_time = np.array(az_time).reshape(self.geolocation(pol)['azimuthTime'].shape) @@ -234,6 +249,7 @@ def geolocation_relative_azimuth_time(self, pol): @lru_cache(maxsize=None) def calibration(self, pol): + """ Import calibrationVector from calibration XML """ calibration = defaultdict(list) for cv in self.xml.calibration[pol].find_all('calibrationVector'): for n in cv: @@ -248,11 +264,9 @@ def calibration(self, pol): @lru_cache(maxsize=None) def aux_calibration_params(self): + """ Import calibrationParams from AUX calibration XML """ swaths = [f'{self.obsMode}{li}' for li in self.swath_ids] - calibration_params = { - 'HH': {swath: {} for swath in swaths}, - 'HV': {swath: {} for swath in swaths} - } + calibration_params = {pol : {swath: {} for swath in swaths} for pol in self.pols} for calibrationParams in self.xml.auxiliary.find_all('calibrationParams'): swath = calibrationParams.swath.text pol = calibrationParams.polarisation.text @@ -268,6 +282,7 @@ def aux_calibration_params(self): @lru_cache(maxsize=None) def noise_range(self, pol): + """ Read range noise vectors from noise XML """ if self.IPFversion < 2.9: noiseRangeVectorName = 'noiseVector' noiseLutName = 'noiseLut' @@ -285,6 +300,7 @@ def noise_range(self, pol): @lru_cache(maxsize=None) def noise_azimuth(self, pol): + """ Read azimuth noise vectors from noise XML """ noise_azimuth = {f'{self.obsMode}{swid}':defaultdict(list) for swid in self.swath_ids} if self.IPFversion < 2.9: for swid in self.swath_ids: @@ -306,6 +322,7 @@ def noise_azimuth(self, pol): return noise_azimuth def get_swath_id_vectors(self, pol, pixel=None): + """ Create vectors with IDs of swaths for each range noise vector """ if pixel is None: pixel = self.noise_range(pol)['pixel'] swath_indices = [np.zeros(p.shape, int) for p in pixel] @@ -353,6 +370,7 @@ def get_eap_rsl_vectors(self, pol, min_size=3, rsl_power=3./2.): return eap_vectors, rsl_vectors def get_pg_product(self, pol, pg_name='pgProductAmplitude'): + """ Read PG-product from annotation XML """ azimuth_time = [(t - self.time_coverage_center).total_seconds() for t in self.noise_range(pol)['azimuthTime']] pg = defaultdict(dict) @@ -384,6 +402,7 @@ def get_tg_vectors(self, pol, min_size=3): return tg_vectors def get_tg_scales_offsets(self): + """ Read scales and offsets of PG-based noise vectors from denosing_parameters.json """ params = self.load_denoising_parameters_json() tg_id = f'{os.path.basename(self.filename)[:16]}_APG_{self.IPFversion:04.2f}' p = params[tg_id] @@ -429,18 +448,24 @@ def get_swath_interpolator(self, pol, swath_name, line, pixel, z): return z_interp2, swath_coords def geolocation_interpolator(self, pol, z): + """ Interpolator of data from geolocation XML """ return RectBivariateSpline( self.geolocation(pol)['line'], self.geolocation(pol)['pixel'], z) def get_angle_vectors(self, pol, angle_name): + """ Get vectors of angles from geolocations for pixels of range noise vectors """ z = self.geolocation(pol)[angle_name] i = self.geolocation_interpolator(pol, z) - angle_vectors = [i(l, p).flatten() for (l, p) in zip(line, pixel)] + angle_vectors = [ + i(l, p).flatten() for (l, p) in + zip(self.noise_range(pol)['line'], self.noise_range(pol)['pixel']) + ] return angle_vectors def get_calibration_vectors(self, pol, name='sigmaNought'): + """ Get calibration constant for pixels of range noise vectors """ line = self.noise_range(pol)['line'] pixel = self.noise_range(pol)['pixel'] swath_names = ['%s%s' % (self.obsMode, iSW) for iSW in self.swath_ids] @@ -621,6 +646,7 @@ def get_raw_sigma0_vectors(self, pol, cal_s0, average_lines=111): return raw_sigma0 def get_raw_sigma0_vectors_from_full_size(self, line, pixel, swath_ids, sigma0_fs, wsy=20, wsx=5, avg_func=np.nanmean): + """ Get values of sigma0 from input array on the pixels of noise range vectors. Averaging in range and azimuth. """ raw_sigma0 = [np.zeros(p.size)+np.nan for p in pixel] for i in range(line.shape[0]): y0 = max(0, line[i]-wsy) @@ -952,6 +978,7 @@ def export_noise_xml(self, pol, output_path): return crosspol_noise_file def get_noise_tg_vectors(self, pol): + """ Compute noise using total gain vectors and precomputed scales and offsets """ pixel = self.noise_range(pol)['pixel'] noise = [np.zeros_like(i) for i in pixel] scales, offsets = self.get_tg_scales_offsets() @@ -964,6 +991,7 @@ def get_noise_tg_vectors(self, pol): return noise def get_nesz_full_size(self, pol, algorithm): + """ Create matrix of NESZ in full resolution for entire scene """ # ESA noise vectors noise = self.noise_range(pol)['noise'] if algorithm == 'NERSC': @@ -989,6 +1017,7 @@ def get_nesz_full_size(self, pol, algorithm): return nesz_fs def remove_thermal_noise(self, pol, algorithm='NERSC', remove_negative=True, min_dn=0): + """ Get calibrated sigma0 and subtract NESZ """ nesz_fs = self.get_nesz_full_size(pol, algorithm) sigma0 = self.get_raw_sigma0_full_size(pol, min_dn=min_dn) sigma0 -= nesz_fs @@ -999,6 +1028,7 @@ def remove_thermal_noise(self, pol, algorithm='NERSC', remove_negative=True, min @lru_cache(maxsize=None) def antenna_pattern(self, pol): + """ Read antennaPattern from annotation XML """ list_keys = ['slantRangeTime', 'elevationAngle', 'elevationPattern', 'incidenceAngle'] antenna_pattern = {} antennaPatternList = self.xml.annotation[pol].find('antennaPatternList') @@ -1052,6 +1082,7 @@ def orbitAtGivenTime(self, pol, relativeAzimuthTime): return orbitAtGivenTime def compute_roll(self, pol, antenna_pattern): + """ Compute roll angle from antenna_pattern """ relativeAzimuthTime = np.array([ (t - self.time_coverage_center).total_seconds() for t in antenna_pattern['azimuthTime'] @@ -1070,6 +1101,7 @@ def compute_roll(self, pol, antenna_pattern): return rollAngle def load_denoising_parameters_json(self): + """ Load scale and offset for NERSC algorithm from JSON file """ denoise_filename = os.path.join( os.path.dirname(os.path.realpath(__file__)), 'denoising_parameters.json') @@ -1146,7 +1178,7 @@ def remove_texture_noise(self, pol, window=3, weight=0.1, s0_min=0, remove_negat Parameters ---------- pol : str - 'HH' or 'HV' + 'HH' or 'HV' or 'or 'VV' 'VH' window : int Size of window in the gaussian filter weight : float @@ -1270,6 +1302,7 @@ def focusedBurstLengthInTime(self, pol): @lru_cache(maxsize=None) def scalloping_gain(self, pol, subswathID): + """ Compute scalloping gain for old data """ # azimuth antenna element patterns (AAEP) lookup table for given subswath gainAAEP = self.aux_calibration_params()[pol][subswathID]['azimuthAntennaPattern'] azimuthAngleIncrement = self.aux_calibration_params()[pol][subswathID]['azimuthAngleIncrement'] @@ -1399,6 +1432,7 @@ def get_scalloping_full_size(self, pol): return scall_fs def get_geolocation_full_size(self, pol, name): + """ Get geolocation array with full resolution for the entire scene """ i = self.geolocation_interpolator(pol, self.geolocation(pol)[name]) rows = np.arange(self.shape(pol)[0]) cols = np.arange(self.shape(pol)[1]) diff --git a/s1denoise/tools.py b/s1denoise/tools.py index 6e28a32..923228c 100644 --- a/s1denoise/tools.py +++ b/s1denoise/tools.py @@ -35,8 +35,8 @@ def run_correction(ifile, Returns -------- - s1 : Nansat - object with corrected bands and metadata + d : dict{np.array} + dictionary with numpy array with corrected sigma0 in dB for both polarisations """ scale = {