def azimuth_y(self, reductionFactor=1):
"""Calculate the angle of each pixel position vector with respect to
the Y-axis (azimuth).
In general, azimuth is the angle from a reference vector (e.g., the
direction to North) to the chosen position vector. The azimuth
increases clockwise from direction to North.
http://en.wikipedia.org/wiki/Azimuth
Parameters
-----------
reductionFactor : integer
factor by which the size of the output array is reduced
Returns
-------
azimuth : numpy array
Values of azimuth in degrees in range 0 - 360
"""
lon_grd, lat_grd = self.get_geolocation_grids(reductionFactor)
a = initial_bearing(lon_grd[1:, :], lat_grd[1:, :], lon_grd[:-1:, :],
lat_grd[:-1:, :])
# Repeat last row once to match size of lon-lat grids
a = np.vstack((a, a[-1, :]))
return a
def add_look_direction_band(self):
lon, lat = self.get_full_size_GCPs()
"""
TODO: Also in mapper_sentinel1_l1.py... Use this code there..
"""
sat_heading = initial_bearing(lon[:-1, :], lat[:-1, :], lon[1:, :],
lat[1:, :])
look_direction = scipy.ndimage.interpolation.zoom(
np.mod(sat_heading + 90, 360),
(np.shape(lon)[0] / (np.shape(lon)[0] - 1.), 1))
# Decompose, to avoid interpolation errors around 0 <-> 360
look_direction_u = np.sin(np.deg2rad(look_direction))
look_direction_v = np.cos(np.deg2rad(look_direction))
look_u_VRT = VRT.from_array(look_direction_u)
look_v_VRT = VRT.from_array(look_direction_v)
lookVRT = VRT.from_lonlat(lon, lat)
lookVRT.create_band([{
'SourceFilename': look_u_VRT.filename,
'SourceBand': 1
}, {
'SourceFilename': look_v_VRT.filename,
'SourceBand': 1
}], {'PixelFunctionType': 'UVToDirectionTo'})
# Blow up to full size
lookVRT = lookVRT.get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize, 1)
# Store VRTs so that they are accessible later
self.band_vrts['look_u_VRT'] = look_u_VRT
self.band_vrts['look_v_VRT'] = look_v_VRT
self.band_vrts['lookVRT'] = lookVRT
src = {
'SourceFilename': self.band_vrts['lookVRT'].filename,
'SourceBand': 1
}
dst = {'wkv': 'sensor_azimuth_angle', 'name': 'look_direction'}
self.create_band(src, dst)
self.dataset.FlushCache()
""" End repetition """
def __init__(self, inputFileName, gdalDataset, gdalMetadata,
xmlonly=False, **kwargs):
''' Create Radarsat2 VRT '''
fPathName, fExt = os.path.splitext(inputFileName)
if zipfile.is_zipfile(inputFileName):
# Open zip file using VSI
fPath, fName = os.path.split(fPathName)
filename = '/vsizip/%s/%s' % (inputFileName, fName)
if not 'RS' in fName[0:2]:
raise WrongMapperError('%s: Provided data is not Radarsat-2'
% fName)
gdalDataset = gdal.Open(filename)
gdalMetadata = gdalDataset.GetMetadata()
else:
filename = inputFileName
# if it is not RADARSAT-2, return
if (not gdalMetadata or not 'SATELLITE_IDENTIFIER' in list(gdalMetadata.keys())):
raise WrongMapperError(filename)
elif gdalMetadata['SATELLITE_IDENTIFIER'] != 'RADARSAT-2':
raise WrongMapperError(filename)
if zipfile.is_zipfile(inputFileName):
# Open product.xml to get additional metadata
zz = zipfile.ZipFile(inputFileName)
productXmlName = os.path.join(os.path.basename(
inputFileName).split('.')[0], 'product.xml')
productXml = zz.open(productXmlName).read()
else:
# product.xml to get additionali metadata
productXmlName = os.path.join(filename, 'product.xml')
if not os.path.isfile(productXmlName):
raise WrongMapperError(filename)
productXml = open(productXmlName).read()
if not IMPORT_SCIPY:
raise NansatReadError('Radarsat-2 data cannot be read because scipy is not installed')
# parse product.XML
rs2_0 = Node.create(productXml)
if xmlonly:
self.init_from_xml(rs2_0, filename)
return
# Get additional metadata from product.xml
rs2_1 = rs2_0.node('sourceAttributes')
rs2_2 = rs2_1.node('radarParameters')
if rs2_2['antennaPointing'].lower() == 'right':
antennaPointing = 90
else:
antennaPointing = -90
rs2_3 = rs2_1.node('orbitAndAttitude').node('orbitInformation')
passDirection = rs2_3['passDirection']
# create empty VRT dataset with geolocation only
self._init_from_gdal_dataset(gdalDataset)
self.dataset.SetGCPs(self.dataset.GetGCPs(), NSR().wkt)
# define dictionary of metadata and band specific parameters
pol = []
metaDict = []
# Get the subdataset with calibrated sigma0 only
for dataset in gdalDataset.GetSubDatasets():
if dataset[1] == 'Sigma Nought calibrated':
s0dataset = gdal.Open(dataset[0])
s0datasetName = dataset[0][:]
band = s0dataset.GetRasterBand(1)
s0datasetPol = band.GetMetadata()['POLARIMETRIC_INTERP']
for i in range(1, s0dataset.RasterCount+1):
iBand = s0dataset.GetRasterBand(i)
polString = iBand.GetMetadata()['POLARIMETRIC_INTERP']
suffix = polString
# The nansat data will be complex
# if the SAR data is of type 10
dtype = iBand.DataType
if dtype == 10:
# add intensity band
metaDict.append(
{'src': {'SourceFilename':
('RADARSAT_2_CALIB:SIGMA0:'
+ filename + '/product.xml'),
'SourceBand': i,
'DataType': dtype},
'dst': {'wkv': 'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'intensity',
'SourceTransferType': gdal.GetDataTypeName(dtype),
'suffix': suffix,
'polarization': polString,
'dataType': 6}})
# modify suffix for adding the compled band below
suffix = polString+'_complex'
pol.append(polString)
metaDict.append(
{'src': {'SourceFilename': ('RADARSAT_2_CALIB:SIGMA0:'
+ filename
+ '/product.xml'),
'SourceBand': i,
'DataType': dtype},
'dst': {'wkv': 'surface_backwards_scattering_coefficient_of_radar_wave',
'suffix': suffix,
'polarization': polString}})
if dataset[1] == 'Beta Nought calibrated':
b0dataset = gdal.Open(dataset[0])
b0datasetName = dataset[0][:]
for j in range(1, b0dataset.RasterCount+1):
jBand = b0dataset.GetRasterBand(j)
polString = jBand.GetMetadata()['POLARIMETRIC_INTERP']
if polString == s0datasetPol:
b0datasetBand = j
###############################
# Add SAR look direction
###############################
d = Domain(ds=gdalDataset)
lon, lat = d.get_geolocation_grids(100)
'''
(GDAL?) Radarsat-2 data is stored with maximum latitude at first
element of each column and minimum longitude at first element of each
row (e.g. np.shape(lat)=(59,55) -> latitude maxima are at lat[0,:],
and longitude minima are at lon[:,0])
In addition, there is an interpolation error for direct estimate along
azimuth. We therefore estimate the heading along range and add 90
degrees to get the "satellite" heading.
'''
if str(passDirection).upper() == 'DESCENDING':
sat_heading = initial_bearing(lon[:, :-1], lat[:, :-1],
lon[:, 1:], lat[:, 1:]) + 90
elif str(passDirection).upper() == 'ASCENDING':
sat_heading = initial_bearing(lon[:, 1:], lat[:, 1:],
lon[:, :-1], lat[:, :-1]) + 90
else:
print('Can not decode pass direction: ' + str(passDirection))
# Calculate SAR look direction
look_direction = sat_heading + antennaPointing
# Interpolate to regain lost row
look_direction = np.mod(look_direction, 360)
look_direction = scipy.ndimage.interpolation.zoom(
look_direction, (1, 11./10.))
# Decompose, to avoid interpolation errors around 0 <-> 360
look_direction_u = np.sin(np.deg2rad(look_direction))
look_direction_v = np.cos(np.deg2rad(look_direction))
look_u_VRT = VRT.from_array(look_direction_u)
look_v_VRT = VRT.from_array(look_direction_v)
# Note: If incidence angle and look direction are stored in
# same VRT, access time is about twice as large
lookVRT = VRT.from_lonlat(lon, lat)
lookVRT.create_band(
[{'SourceFilename': look_u_VRT.filename, 'SourceBand': 1},
{'SourceFilename': look_v_VRT.filename, 'SourceBand': 1}],
{'PixelFunctionType': 'UVToDirectionTo'})
# Blow up to full size
lookVRT = lookVRT.get_resized_vrt(gdalDataset.RasterXSize, gdalDataset.RasterYSize)
# Store VRTs so that they are accessible later
self.band_vrts['look_u_VRT'] = look_u_VRT
self.band_vrts['look_v_VRT'] = look_v_VRT
self.band_vrts['lookVRT'] = lookVRT
# Add band to full sized VRT
lookFileName = self.band_vrts['lookVRT'].filename
metaDict.append({'src': {'SourceFilename': lookFileName,
'SourceBand': 1},
'dst': {'wkv': 'sensor_azimuth_angle',
'name': 'look_direction'}})
###############################
# Create bands
###############################
self.create_bands(metaDict)
###################################################
# Add derived band (incidence angle) calculated
# using pixel function "BetaSigmaToIncidence":
###################################################
src = [{'SourceFilename': b0datasetName,
'SourceBand': b0datasetBand,
'DataType': dtype},
{'SourceFilename': s0datasetName,
'SourceBand': 1,
'DataType': dtype}]
dst = {'wkv': 'angle_of_incidence',
'PixelFunctionType': 'BetaSigmaToIncidence',
'SourceTransferType': gdal.GetDataTypeName(dtype),
'_FillValue': -10000, # NB: this is also hard-coded in
# pixelfunctions.c
'dataType': 6,
'name': 'incidence_angle'}
self.create_band(src, dst)
self.dataset.FlushCache()
###################################################################
# Add sigma0_VV - pixel function of sigma0_HH and beta0_HH
# incidence angle is calculated within pixel function
# It is assummed that HH is the first band in sigma0 and
# beta0 sub datasets
###################################################################
if 'VV' not in pol and 'HH' in pol:
s0datasetNameHH = pol.index('HH')+1
src = [{'SourceFilename': s0datasetName,
'SourceBand': s0datasetNameHH,
'DataType': 6},
{'SourceFilename': b0datasetName,
'SourceBand': b0datasetBand,
'DataType': 6}]
dst = {'wkv': 'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'Sigma0HHBetaToSigma0VV',
'polarization': 'VV',
'suffix': 'VV'}
self.create_band(src, dst)
self.dataset.FlushCache()
############################################
# Add SAR metadata
############################################
if antennaPointing == 90:
self.dataset.SetMetadataItem('ANTENNA_POINTING', 'RIGHT')
if antennaPointing == -90:
self.dataset.SetMetadataItem('ANTENNA_POINTING', 'LEFT')
self.dataset.SetMetadataItem('ORBIT_DIRECTION',
str(passDirection).upper())
# set valid time
self.dataset.SetMetadataItem('time_coverage_start',
(parse(gdalMetadata['FIRST_LINE_TIME']).
isoformat()))
self.dataset.SetMetadataItem('time_coverage_end',
(parse(gdalMetadata['LAST_LINE_TIME']).
isoformat()))
# Get dictionary describing the instrument and platform according to
# the GCMD keywords
mm = pti.get_gcmd_instrument("C-SAR")
ee = pti.get_gcmd_platform('radarsat-2')
# TODO: Validate that the found instrument and platform are indeed what we
# want....
self.dataset.SetMetadataItem('instrument', json.dumps(mm))
self.dataset.SetMetadataItem('platform', json.dumps(ee))
self.dataset.SetMetadataItem('entry_title', 'Radarsat-2 SAR')
self.dataset.SetMetadataItem('provider', 'MDA/GSI')
self.dataset.SetMetadataItem('dataset_parameters', json.dumps(
['surface_backwards_scattering_coefficient_of_radar_wave']))
self.dataset.SetMetadataItem('entry_id', os.path.basename(filename))
def __init__(self, filename, gdalDataset, gdalMetadata, **kwargs):
'''
Parameters
-----------
filename : string
gdalDataset : gdal dataset
gdalMetadata : gdal metadata
'''
self.setup_ads_parameters(filename, gdalMetadata)
if self.product[0:4] != "ASA_":
raise WrongMapperError
if not IMPORT_SCIPY:
raise NansatReadError(
'ASAR data cannot be read because scipy is not installed')
# get channel string (remove '/', since NetCDF
# does not support that in metadata)
polarization = [{
'channel':
gdalMetadata['SPH_MDS1_TX_RX_POLAR'].replace("/", ""),
'bandNum':
1
}]
# if there is the 2nd band, get channel string
if 'SPH_MDS2_TX_RX_POLAR' in gdalMetadata.keys():
channel = gdalMetadata['SPH_MDS2_TX_RX_POLAR'].replace("/", "")
if not (channel.isspace()):
polarization.append({'channel': channel, 'bandNum': 2})
# create empty VRT dataset with geolocation only
self._init_from_gdal_dataset(gdalDataset, metadata=gdalMetadata)
# get calibration constant
gotCalibration = True
try:
for iPolarization in polarization:
metaKey = (
'MAIN_PROCESSING_PARAMS_ADS_CALIBRATION_FACTORS.%d.EXT_CAL_FACT'
% (iPolarization['bandNum']))
iPolarization['calibrationConst'] = float(
gdalDataset.GetMetadataItem(metaKey, 'records'))
except:
try:
for iPolarization in polarization:
# Apparently some ASAR files have calibration
# constant stored in another place
metaKey = (
'MAIN_PROCESSING_PARAMS_ADS_0_CALIBRATION_FACTORS.%d.EXT_CAL_FACT'
% (iPolarization['bandNum']))
iPolarization['calibrationConst'] = float(
gdalDataset.GetMetadataItem(metaKey, 'records'))
except:
self.logger.warning('Cannot get calibrationConst')
gotCalibration = False
# add dictionary for raw counts
metaDict = []
for iPolarization in polarization:
iBand = gdalDataset.GetRasterBand(iPolarization['bandNum'])
dtype = iBand.DataType
shortName = 'RawCounts_%s' % iPolarization['channel']
bandName = shortName
dstName = 'raw_counts_%s' % iPolarization['channel']
if (8 <= dtype and dtype < 12):
bandName = shortName + '_complex'
dstName = dstName + '_complex'
metaDict.append({
'src': {
'SourceFilename': filename,
'SourceBand': iPolarization['bandNum']
},
'dst': {
'name': dstName
}
})
'''
metaDict.append({'src': {'SourceFilename': filename,
'SourceBand': iPolarization['bandNum']},
'dst': {'name': 'raw_counts_%s'
% iPolarization['channel']}})
'''
# if raw data is complex, add the intensity band
if (8 <= dtype and dtype < 12):
# choose pixelfunction type
if (dtype == 8 or dtype == 9):
pixelFunctionType = 'IntensityInt'
else:
pixelFunctionType = 'intensity'
# get data type of the intensity band
intensityDataType = {
'8': 3,
'9': 4,
'10': 5,
'11': 6
}.get(str(dtype), 4)
# add intensity band
metaDict.append({
'src': {
'SourceFilename': filename,
'SourceBand': iPolarization['bandNum'],
'DataType': dtype
},
'dst': {
'name': 'raw_counts_%s' % iPolarization['channel'],
'PixelFunctionType': pixelFunctionType,
'SourceTransferType': gdal.GetDataTypeName(dtype),
'dataType': intensityDataType
}
})
#####################################################################
# Add incidence angle and look direction through small VRT objects
#####################################################################
lon = self.get_array_from_ADS('first_line_longs')
lat = self.get_array_from_ADS('first_line_lats')
inc = self.get_array_from_ADS('first_line_incidence_angle')
# Calculate SAR look direction (ASAR is always right-looking)
look_direction = initial_bearing(lon[:, :-1], lat[:, :-1], lon[:, 1:],
lat[:, 1:])
# Interpolate to regain lost row
look_direction = scipy.ndimage.interpolation.zoom(
look_direction, (1, 11. / 10.))
# Decompose, to avoid interpolation errors around 0 <-> 360
look_direction_u = np.sin(np.deg2rad(look_direction))
look_direction_v = np.cos(np.deg2rad(look_direction))
look_u_VRT = VRT.from_array(look_direction_u)
look_v_VRT = VRT.from_array(look_direction_v)
# Note: If incidence angle and look direction are stored in
# same VRT, access time is about twice as large
incVRT = VRT.from_array(inc)
lookVRT = VRT.from_lonlat(lon, lat)
lookVRT.create_band([{
'SourceFilename': look_u_VRT.filename,
'SourceBand': 1
}, {
'SourceFilename': look_v_VRT.filename,
'SourceBand': 1
}], {'PixelFunctionType': 'UVToDirectionTo'})
# Blow up bands to full size
incVRT = incVRT.get_resized_vrt(gdalDataset.RasterXSize,
gdalDataset.RasterYSize)
lookVRT = lookVRT.get_resized_vrt(gdalDataset.RasterXSize,
gdalDataset.RasterYSize)
# Store VRTs so that they are accessible later
self.band_vrts = {
'incVRT': incVRT,
'look_u_VRT': look_u_VRT,
'look_v_VRT': look_v_VRT,
'lookVRT': lookVRT
}
# Add band to full sized VRT
incFileName = self.band_vrts['incVRT'].filename
lookFileName = self.band_vrts['lookVRT'].filename
metaDict.append({
'src': {
'SourceFilename': incFileName,
'SourceBand': 1
},
'dst': {
'wkv': 'angle_of_incidence',
'name': 'incidence_angle'
}
})
metaDict.append({
'src': {
'SourceFilename': lookFileName,
'SourceBand': 1
},
'dst': {
'wkv': 'sensor_azimuth_angle',
'name': 'look_direction'
}
})
####################
# Add Sigma0-bands
####################
if gotCalibration:
for iPolarization in polarization:
# add dictionary for sigma0, ice and water
short_names = [
'sigma0', 'sigma0_normalized_ice',
'sigma0_normalized_water'
]
wkt = [
'surface_backwards_scattering_coefficient_of_radar_wave',
'surface_backwards_scattering_coefficient_of_radar_wave_normalized_over_ice',
'surface_backwards_scattering_coefficient_of_radar_wave_normalized_over_water'
]
sphPass = [gdalMetadata['SPH_PASS'], '', '']
sourceFileNames = [filename, incFileName]
pixelFunctionTypes = [
'RawcountsIncidenceToSigma0', 'Sigma0NormalizedIce'
]
if iPolarization['channel'] == 'HH':
pixelFunctionTypes.append('Sigma0HHNormalizedWater')
elif iPolarization['channel'] == 'VV':
pixelFunctionTypes.append('Sigma0VVNormalizedWater')
# add pixelfunction bands to metaDict
for iPixFunc in range(len(pixelFunctionTypes)):
srcFiles = []
for j, jFileName in enumerate(sourceFileNames):
sourceFile = {'SourceFilename': jFileName}
if j == 0:
sourceFile['SourceBand'] = iPolarization['bandNum']
# if ASA_full_incAng,
# set 'ScaleRatio' into source file dict
sourceFile['ScaleRatio'] = np.sqrt(
1.0 / iPolarization['calibrationConst'])
else:
sourceFile['SourceBand'] = 1
srcFiles.append(sourceFile)
metaDict.append({
'src': srcFiles,
'dst': {
'short_name': short_names[iPixFunc],
'wkv': wkt[iPixFunc],
'PixelFunctionType':
(pixelFunctionTypes[iPixFunc]),
'polarization': iPolarization['channel'],
'suffix': iPolarization['channel'],
'pass': sphPass[iPixFunc],
'dataType': 6
}
})
# add bands with metadata and corresponding values to the empty VRT
self.create_bands(metaDict)
# Add oribit and look information to metadata domain
# ASAR is always right-looking
self.dataset.SetMetadataItem('ANTENNA_POINTING', 'RIGHT')
self.dataset.SetMetadataItem('ORBIT_DIRECTION',
gdalMetadata['SPH_PASS'].upper().strip())
###################################################################
# Estimate sigma0_VV from sigma0_HH
###################################################################
polarizations = []
for pp in polarization:
polarizations.append(pp['channel'])
if 'VV' not in polarizations and 'HH' in polarizations:
srcFiles = []
for j, jFileName in enumerate(sourceFileNames):
sourceFile = {'SourceFilename': jFileName}
if j == 0:
sourceFile['SourceBand'] = iPolarization['bandNum']
# if ASA_full_incAng,
# set 'ScaleRatio' into source file dict
sourceFile['ScaleRatio'] = np.sqrt(
1.0 / iPolarization['calibrationConst'])
else:
sourceFile['SourceBand'] = 1
srcFiles.append(sourceFile)
dst = {
'wkv':
('surface_backwards_scattering_coefficient_of_radar_wave'),
'PixelFunctionType': 'Sigma0HHToSigma0VV',
'polarization': 'VV',
'suffix': 'VV'
}
self.create_band(srcFiles, dst)
self.dataset.FlushCache()
# set time
self._set_envisat_time(gdalMetadata)
# When using TPS for reprojection, use only every 3rd GCP
# to improve performance (tradeoff vs accuracy)
self.dataset.SetMetadataItem('skip_gcps', '3')
self.dataset.SetMetadataItem(
'time_coverage_start',
(parse(gdalMetadata['MPH_SENSING_START']).isoformat()))
self.dataset.SetMetadataItem(
'time_coverage_end',
(parse(gdalMetadata['MPH_SENSING_STOP']).isoformat()))
# Get dictionary describing the instrument and platform according to
# the GCMD keywords
mm = pti.get_gcmd_instrument('asar')
ee = pti.get_gcmd_platform('envisat')
# TODO: Validate that the found instrument and platform are indeed what
# we want....
self.dataset.SetMetadataItem('instrument', json.dumps(mm))
self.dataset.SetMetadataItem('platform', json.dumps(ee))
def __init__(self, filename, gdalDataset, gdalMetadata, fast=False, fixgcp=True, **kwargs):
if not os.path.split(filename.rstrip('/'))[1][:3] in ['S1A', 'S1B']:
raise WrongMapperError('%s: Not Sentinel 1A or 1B' %filename)
if not IMPORT_SCIPY:
raise NansatReadError('Sentinel-1 data cannot be read because scipy is not installed')
if zipfile.is_zipfile(filename):
zz = zipfile.PyZipFile(filename)
# Assuming the file names are consistent, the polarization
# dependent data should be sorted equally such that we can use the
# same indices consistently for all the following lists
# THIS IS NOT THE CASE...
mds_files = ['/vsizip/%s/%s' % (filename, fn)
for fn in zz.namelist() if 'measurement/s1' in fn]
calibration_files = ['/vsizip/%s/%s' % (filename, fn)
for fn in zz.namelist()
if 'annotation/calibration/calibration-s1' in fn]
noise_files = ['/vsizip/%s/%s' % (filename, fn)
for fn in zz.namelist()
if 'annotation/calibration/noise-s1' in fn]
annotation_files = ['/vsizip/%s/%s' % (filename, fn)
for fn in zz.namelist()
if 'annotation/s1' in fn]
manifest_files = ['/vsizip/%s/%s' % (filename, fn)
for fn in zz.namelist()
if 'manifest.safe' in fn]
zz.close()
else:
mds_files = glob.glob('%s/measurement/s1*' % filename)
calibration_files = glob.glob('%s/annotation/calibration/calibration-s1*'
% filename)
noise_files = glob.glob('%s/annotation/calibration/noise-s1*'
% filename)
annotation_files = glob.glob('%s/annotation/s1*'
% filename)
manifest_files = glob.glob('%s/manifest.safe' % filename)
if (not mds_files or not calibration_files or not noise_files or
not annotation_files or not manifest_files):
raise WrongMapperError(filename)
# convert list of MDS files into dictionary. Keys - polarizations in upper case.
mds_files = {os.path.basename(ff).split('-')[3].upper():ff for ff in mds_files}
polarizations = list(mds_files.keys())
# read annotation files
self.annotation_data = self.read_annotation(annotation_files)
if not fast and fixgcp:
self.correct_geolocation_data()
# read manifest file
manifest_data = self.read_manifest_data(manifest_files[0])
# very fast constructor without any bands only with some metadata and geolocation
self._init_empty(manifest_data, self.annotation_data, filename)
# skip adding bands in the fast mode and RETURN
if fast:
return
# Open data files with GDAL
gdalDatasets = {}
for pol in polarizations:
gdalDatasets[pol] = gdal.Open(mds_files[pol])
if not gdalDatasets[pol]:
raise WrongMapperError('%s: No Sentinel-1 datasets found' % mds_files[pol])
# Check metadata to confirm it is Sentinel-1 L1
metadata = gdalDatasets[polarizations[0]].GetMetadata()
# create full size VRTs with incidenceAngle and elevationAngle
annotation_vrts = self.vrts_from_arrays(self.annotation_data,
['incidenceAngle', 'elevationAngle'])
self.band_vrts.update(annotation_vrts)
# create full size VRTS with calibration LUT
calibration_names = ['sigmaNought', 'betaNought']
calibration_list_tag = 'calibrationVectorList'
for calibration_file in calibration_files:
pol = '_' + os.path.basename(calibration_file).split('-')[4].upper()
xml = self.read_vsi(calibration_file)
calibration_data = self.read_calibration(xml, calibration_list_tag, calibration_names, pol)
calibration_vrts = self.vrts_from_arrays(calibration_data, calibration_names, pol, True, 1)
self.band_vrts.update(calibration_vrts)
# create full size VRTS with noise LUT
for noise_file in noise_files:
pol = '_' + os.path.basename(noise_file).split('-')[4].upper()
xml = self.read_vsi(noise_file)
if '<noiseVectorList' in xml:
noise_list_tag = 'noiseVectorList'
noise_name = 'noiseLut'
elif '<noiseRangeVectorList' in xml:
noise_list_tag = 'noiseRangeVectorList'
noise_name = 'noiseRangeLut'
noise_data = self.read_calibration(xml, noise_list_tag, [noise_name], pol)
noise_vrts = self.vrts_from_arrays(noise_data, [noise_name], pol, True, 1)
self.band_vrts.update(noise_vrts)
#### Create metaDict: dict with metadata for all bands
metaDict = []
bandNumberDict = {}
bnmax = 0
for pol in polarizations:
dsPath, dsName = os.path.split(mds_files[pol])
name = 'DN_%s' % pol
# A dictionary of band numbers is needed for the pixel function
# bands further down. This is not the best solution. It would be
# better to have a function in VRT that returns the number given a
# band name. This function exists in Nansat but could perhaps be
# moved to VRT? The existing nansat function could just call the
# VRT one...
bandNumberDict[name] = bnmax + 1
bnmax = bandNumberDict[name]
band = gdalDatasets[pol].GetRasterBand(1)
dtype = band.DataType
metaDict.append({
'src': {
'SourceFilename': mds_files[pol],
'SourceBand': 1,
'DataType': dtype,
},
'dst': {
'name': name,
},
})
# add bands with metadata and corresponding values to the empty VRT
self.create_bands(metaDict)
'''
Calibration should be performed as
s0 = DN^2/sigmaNought^2,
where sigmaNought is from e.g.
annotation/calibration/calibration-s1a-iw-grd-hh-20140811t151231-20140811t151301-001894-001cc7-001.xml,
and DN is the Digital Numbers in the tiff files.
Also the noise should be subtracted.
See
https://sentinel.esa.int/web/sentinel/sentinel-1-sar-wiki/-/wiki/Sentinel%20One/Application+of+Radiometric+Calibration+LUT
The noise correction/subtraction is implemented in an independent package "sentinel1denoised"
See
https://github.com/nansencenter/sentinel1denoised
'''
# Get look direction
longitude, latitude = self.transform_points(calibration_data['pixel'].flatten(),
calibration_data['line'].flatten())
longitude.shape = calibration_data['pixel'].shape
latitude.shape = calibration_data['pixel'].shape
sat_heading = initial_bearing(longitude[:-1, :],
latitude[:-1, :],
longitude[1:, :],
latitude[1:, :])
look_direction = scipy.ndimage.interpolation.zoom(
np.mod(sat_heading + 90, 360),
(np.shape(longitude)[0] / (np.shape(longitude)[0]-1.), 1))
# Decompose, to avoid interpolation errors around 0 <-> 360
look_direction_u = np.sin(np.deg2rad(look_direction))
look_direction_v = np.cos(np.deg2rad(look_direction))
look_u_VRT = VRT.from_array(look_direction_u)
look_v_VRT = VRT.from_array(look_direction_v)
lookVRT = VRT.from_lonlat(longitude, latitude)
lookVRT.create_band([{'SourceFilename': look_u_VRT.filename,
'SourceBand': 1},
{'SourceFilename': look_v_VRT.filename,
'SourceBand': 1}],
{'PixelFunctionType': 'UVToDirectionTo'}
)
# Blow up to full size
lookVRT = lookVRT.get_resized_vrt(self.dataset.RasterXSize, self.dataset.RasterYSize, 1)
# Store VRTs so that they are accessible later
self.band_vrts['look_u_VRT'] = look_u_VRT
self.band_vrts['look_v_VRT'] = look_v_VRT
self.band_vrts['lookVRT'] = lookVRT
metaDict = []
# Add bands to full size VRT
for pol in polarizations:
name = 'sigmaNought_%s' % pol
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': {'SourceFilename':
(self.band_vrts[name].filename),
'SourceBand': 1
},
'dst': {'name': name
}
})
name = 'noise_%s' % pol
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append({
'src': {
'SourceFilename': self.band_vrts['%s_%s' % (noise_name, pol)].filename,
'SourceBand': 1
},
'dst': {
'name': name
}
})
name = 'look_direction'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append({
'src': {
'SourceFilename': self.band_vrts['lookVRT'].filename,
'SourceBand': 1
},
'dst': {
'wkv': 'sensor_azimuth_angle',
'name': name
}
})
for pol in polarizations:
dsPath, dsName = os.path.split(mds_files[pol])
name = 'sigma0_%s' % pol
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': [{'SourceFilename': self.filename,
'SourceBand': bandNumberDict['DN_%s' % pol],
},
{'SourceFilename': self.band_vrts['sigmaNought_%s' % pol].filename,
'SourceBand': 1
}
],
'dst': {'wkv': 'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Calibration',
'polarization': pol,
'suffix': pol,
},
})
name = 'beta0_%s' % pol
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': [{'SourceFilename': self.filename,
'SourceBand': bandNumberDict['DN_%s' % pol]
},
{'SourceFilename': self.band_vrts['betaNought_%s' % pol].filename,
'SourceBand': 1
}
],
'dst': {'wkv': 'surface_backwards_brightness_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Calibration',
'polarization': pol,
'suffix': pol,
},
})
self.create_bands(metaDict)
# Add incidence angle as band
name = 'incidence_angle'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
src = {'SourceFilename': self.band_vrts['incidenceAngle'].filename,
'SourceBand': 1}
dst = {'wkv': 'angle_of_incidence',
'name': name}
self.create_band(src, dst)
self.dataset.FlushCache()
# Add elevation angle as band
name = 'elevation_angle'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
src = {'SourceFilename': self.band_vrts['elevationAngle'].filename,
'SourceBand': 1}
dst = {'wkv': 'angle_of_elevation',
'name': name}
self.create_band(src, dst)
self.dataset.FlushCache()
# Add sigma0_VV
if 'VV' not in polarizations and 'HH' in polarizations:
name = 'sigma0_VV'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
src = [{'SourceFilename': self.filename,
'SourceBand': bandNumberDict['DN_HH'],
},
{'SourceFilename': (self.band_vrts['sigmaNought_HH'].
filename),
'SourceBand': 1,
},
{'SourceFilename': self.band_vrts['incidenceAngle'].filename,
'SourceBand': 1}
]
dst = {'wkv': 'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Sigma0HHToSigma0VV',
'polarization': 'VV',
'suffix': 'VV'}
self.create_band(src, dst)
self.dataset.FlushCache()
def initial_bearing(lon1, lat1, lon2, lat2):
return utils.initial_bearing(lon1, lat1, lon2, lat2)