def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
''' Create Radarsat2 VRT '''
fPathName, fExt = os.path.splitext(fileName)
if zipfile.is_zipfile(fileName):
# Open zip file using VSI
fPath, fName = os.path.split(fPathName)
fileName = '/vsizip/%s/%s' % (fileName, fName)
if not 'RS' in fName[0:2]:
raise WrongMapperError('Provided data is not Radarsat-2')
gdalDataset = gdal.Open(fileName)
gdalMetadata = gdalDataset.GetMetadata()
#if it is not RADARSAT-2, return
if (not gdalMetadata
or not 'SATELLITE_IDENTIFIER' in gdalMetadata.keys()):
raise WrongMapperError
elif gdalMetadata['SATELLITE_IDENTIFIER'] != 'RADARSAT-2':
raise WrongMapperError
# read product.xml
productXmlName = os.path.join(fileName, 'product.xml')
productXml = self.read_xml(productXmlName)
# Get additional metadata from product.xml
rs2_0 = Node.create(productXml)
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
VRT.__init__(self, gdalDataset)
#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
SAR_look_direction = sat_heading + antennaPointing
# Interpolate to regain lost row
SAR_look_direction = np.mod(SAR_look_direction, 360)
SAR_look_direction = scipy.ndimage.interpolation.zoom(
SAR_look_direction, (1, 11. / 10.))
# Decompose, to avoid interpolation errors around 0 <-> 360
SAR_look_direction_u = np.sin(np.deg2rad(SAR_look_direction))
SAR_look_direction_v = np.cos(np.deg2rad(SAR_look_direction))
look_u_VRT = VRT(array=SAR_look_direction_u, lat=lat, lon=lon)
look_v_VRT = VRT(array=SAR_look_direction_v, lat=lat, lon=lon)
# Note: If incidence angle and look direction are stored in
# same VRT, access time is about twice as large
lookVRT = VRT(lat=lat, lon=lon)
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.bandVRTs['look_u_VRT'] = look_u_VRT
self.bandVRTs['look_v_VRT'] = look_v_VRT
self.bandVRTs['lookVRT'] = lookVRT
# Add band to full sized VRT
lookFileName = self.bandVRTs['lookVRT'].fileName
metaDict.append({
'src': {
'SourceFilename': lookFileName,
'SourceBand': 1
},
'dst': {
'wkv': 'sensor_azimuth_angle',
'name': 'SAR_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 time
validTime = gdalDataset.GetMetadata()['ACQUISITION_START_TIME']
self.logger.info('Valid time: %s', str(validTime))
self._set_time(parse(validTime))
# set SADCAT specific metadata
self.dataset.SetMetadataItem(
'start_date', (parse(gdalMetadata['FIRST_LINE_TIME']).isoformat()))
self.dataset.SetMetadataItem(
'stop_date', (parse(gdalMetadata['LAST_LINE_TIME']).isoformat()))
self.dataset.SetMetadataItem('sensor', 'SAR')
self.dataset.SetMetadataItem('satellite', 'Radarsat2')
self.dataset.SetMetadataItem('mapper', 'radarsat2')
def __init__(self, fileName, gdalDataset, gdalMetadata,
manifestonly=False, **kwargs):
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...
mdsFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist() if 'measurement/s1a' in fn]
calFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'annotation/calibration/calibration-s1a' in fn]
noiseFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'annotation/calibration/noise-s1a' in fn]
annotationFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'annotation/s1a' in fn]
manifestFile = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'manifest.safe' in fn]
zz.close()
else:
mdsFiles = glob.glob('%s/measurement/s1a*' % fileName)
calFiles = glob.glob('%s/annotation/calibration/calibration-s1a*'
% fileName)
noiseFiles = glob.glob('%s/annotation/calibration/noise-s1a*'
% fileName)
annotationFiles = glob.glob('%s/annotation/s1a*'
% fileName)
manifestFile = glob.glob('%s/manifest.safe' % fileName)
if (not mdsFiles or not calFiles or not noiseFiles or
not annotationFiles or not manifestFile):
raise WrongMapperError
mdsDict = {}
for ff in mdsFiles:
mdsDict[
os.path.splitext(os.path.basename(ff))[0].split('-')[3]] = ff
self.calXMLDict = {}
for ff in calFiles:
self.calXMLDict[
os.path.splitext(
os.path.basename(ff))[0].split('-')[4]] = self.read_xml(ff)
self.noiseXMLDict = {}
for ff in noiseFiles:
self.noiseXMLDict[
os.path.splitext(
os.path.basename(ff))[0].split('-')[4]] = self.read_xml(ff)
self.annotationXMLDict = {}
for ff in annotationFiles:
self.annotationXMLDict[
os.path.splitext(
os.path.basename(ff))[0].split('-')[3]] = self.read_xml(ff)
self.manifestXML = self.read_xml(manifestFile[0])
# very fast constructor without any bands
if manifestonly:
self.init_from_manifest_only(self.manifestXML,
self.annotationXMLDict[
self.annotationXMLDict.keys()[0]])
return
gdalDatasets = {}
for key in mdsDict.keys():
# Open data files
gdalDatasets[key] = gdal.Open(mdsDict[key])
if not gdalDatasets:
raise WrongMapperError('No Sentinel-1 datasets found')
# Check metadata to confirm it is Sentinel-1 L1
for key in gdalDatasets:
metadata = gdalDatasets[key].GetMetadata()
break
if not 'TIFFTAG_IMAGEDESCRIPTION' in metadata.keys():
raise WrongMapperError
if (not 'Sentinel-1' in metadata['TIFFTAG_IMAGEDESCRIPTION']
and not 'L1' in metadata['TIFFTAG_IMAGEDESCRIPTION']):
raise WrongMapperError
warnings.warn('Sentinel-1 level-1 mapper is not yet adapted to '
'complex data. In addition, the band names should be '
'updated for multi-swath data - '
'and there might be other issues.')
# create empty VRT dataset with geolocation only
for key in gdalDatasets:
VRT.__init__(self, gdalDatasets[key])
break
# Read annotation, noise and calibration xml-files
pol = {}
it = 0
for key in self.annotationXMLDict:
xml = Node.create(self.annotationXMLDict[key])
pol[key] = (xml.node('product').
node('adsHeader')['polarisation'].upper())
it += 1
if it == 1:
# Get incidence angle
pi = xml.node('generalAnnotation').node('productInformation')
self.dataset.SetMetadataItem('ORBIT_DIRECTION',
str(pi['pass']))
(X, Y, lon, lat, inc, ele, numberOfSamples,
numberOfLines) = self.read_geolocation_lut(
self.annotationXMLDict[key])
X = np.unique(X)
Y = np.unique(Y)
lon = np.array(lon).reshape(len(Y), len(X))
lat = np.array(lat).reshape(len(Y), len(X))
inc = np.array(inc).reshape(len(Y), len(X))
ele = np.array(ele).reshape(len(Y), len(X))
incVRT = VRT(array=inc, lat=lat, lon=lon)
eleVRT = VRT(array=ele, lat=lat, lon=lon)
incVRT = incVRT.get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=2)
eleVRT = eleVRT.get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=2)
self.bandVRTs['incVRT'] = incVRT
self.bandVRTs['eleVRT'] = eleVRT
for key in self.calXMLDict:
calibration_LUT_VRTs, longitude, latitude = (
self.get_LUT_VRTs(self.calXMLDict[key],
'calibrationVectorList',
['sigmaNought', 'betaNought',
'gamma', 'dn']
))
self.bandVRTs['LUT_sigmaNought_VRT_'+pol[key]] = (
calibration_LUT_VRTs['sigmaNought'].
get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=1))
self.bandVRTs['LUT_betaNought_VRT_'+pol[key]] = (
calibration_LUT_VRTs['betaNought'].
get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=1))
self.bandVRTs['LUT_gamma_VRT'] = calibration_LUT_VRTs['gamma']
self.bandVRTs['LUT_dn_VRT'] = calibration_LUT_VRTs['dn']
for key in self.noiseXMLDict:
noise_LUT_VRT = self.get_LUT_VRTs(self.noiseXMLDict[key],
'noiseVectorList',
['noiseLut'])[0]
self.bandVRTs['LUT_noise_VRT_'+pol[key]] = (
noise_LUT_VRT['noiseLut'].get_resized_vrt(
self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=1))
metaDict = []
bandNumberDict = {}
bnmax = 0
for key in gdalDatasets.keys():
dsPath, dsName = os.path.split(mdsDict[key])
name = 'DN_%s' % pol[key]
# 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[key].GetRasterBand(1)
dtype = band.DataType
metaDict.append({
'src': {
'SourceFilename': mdsDict[key],
'SourceBand': 1,
'DataType': dtype,
},
'dst': {
'name': name,
#'SourceTransferType': gdal.GetDataTypeName(dtype),
#'dataType': 6,
},
})
# 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
'''
# Get look direction
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(array=look_direction_u,
lat=latitude, lon=longitude)
look_v_VRT = VRT(array=look_direction_v,
lat=latitude, lon=longitude)
lookVRT = VRT(lat=latitude, lon=longitude)
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,
eResampleAlg=1)
# Store VRTs so that they are accessible later
self.bandVRTs['look_u_VRT'] = look_u_VRT
self.bandVRTs['look_v_VRT'] = look_v_VRT
self.bandVRTs['lookVRT'] = lookVRT
metaDict = []
# Add bands to full size VRT
for key in pol:
name = 'LUT_sigmaNought_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': {'SourceFilename':
(self.bandVRTs['LUT_sigmaNought_VRT_' +
pol[key]].fileName),
'SourceBand': 1
},
'dst': {'name': name
}
})
name = 'LUT_noise_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append({
'src': {
'SourceFilename': self.bandVRTs['LUT_noise_VRT_' +
pol[key]].fileName,
'SourceBand': 1
},
'dst': {
'name': name
}
})
name = 'look_direction'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append({
'src': {
'SourceFilename': self.bandVRTs['lookVRT'].fileName,
'SourceBand': 1
},
'dst': {
'wkv': 'sensor_azimuth_angle',
'name': name
}
})
for key in gdalDatasets.keys():
dsPath, dsName = os.path.split(mdsDict[key])
name = 'sigma0_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': [{'SourceFilename': self.fileName,
'SourceBand': bandNumberDict['DN_%s' % pol[key]],
},
{'SourceFilename':
(self.bandVRTs['LUT_sigmaNought_VRT_%s'
% pol[key]].fileName),
'SourceBand': 1
}
],
'dst': {'wkv': 'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Calibration',
'polarization': pol[key],
'suffix': pol[key],
},
})
name = 'beta0_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': [{'SourceFilename': self.fileName,
'SourceBand': bandNumberDict['DN_%s' % pol[key]]
},
{'SourceFilename':
(self.bandVRTs['LUT_betaNought_VRT_%s'
% pol[key]].fileName),
'SourceBand': 1
}
],
'dst': {'wkv': 'surface_backwards_brightness_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Calibration',
'polarization': pol[key],
'suffix': pol[key],
},
})
self._create_bands(metaDict)
# Add incidence angle as band
name = 'incidence_angle'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
src = {'SourceFilename': self.bandVRTs['incVRT'].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.bandVRTs['eleVRT'].fileName,
'SourceBand': 1}
dst = {'wkv': 'angle_of_elevation',
'name': name}
self._create_band(src, dst)
self.dataset.FlushCache()
# Add sigma0_VV
pp = [pol[key] for key in pol]
if 'VV' not in pp and 'HH' in pp:
name = 'sigma0_VV'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
src = [{'SourceFilename': self.fileName,
'SourceBand': bandNumberDict['DN_HH'],
},
{'SourceFilename': (self.bandVRTs['LUT_noise_VRT_HH'].
fileName),
'SourceBand': 1
},
{'SourceFilename': (self.bandVRTs['LUT_sigmaNought_VRT_HH'].
fileName),
'SourceBand': 1,
},
{'SourceFilename': self.bandVRTs['incVRT'].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()
# set time as acquisition start time
n = Node.create(self.manifestXML)
meta = n.node('metadataSection')
for nn in meta.children:
if nn.getAttribute('ID') == u'acquisitionPeriod':
# set valid time
self.dataset.SetMetadataItem(
'time_coverage_start',
parse((nn.node('metadataWrap').
node('xmlData').
node('safe:acquisitionPeriod')['safe:startTime'])
).isoformat())
self.dataset.SetMetadataItem(
'time_coverage_end',
parse((nn.node('metadataWrap').
node('xmlData').
node('safe:acquisitionPeriod')['safe:stopTime'])
).isoformat())
# Get dictionary describing the instrument and platform according to
# the GCMD keywords
mm = pti.get_gcmd_instrument('sar')
ee = pti.get_gcmd_platform('sentinel-1a')
# 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, **kwargs):
'''
Parameters
-----------
fileName : string
gdalDataset : gdal dataset
gdalMetadata : gdal metadata
'''
self.setup_ads_parameters(fileName, gdalMetadata)
if self.product[0:4] != "ASA_":
raise WrongMapperError
# 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
VRT.__init__(self, gdalDataset)
# 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:
metaDict.append({'src': {'SourceFilename': fileName,
'SourceBand': iPolarization['bandNum']},
'dst': {'name': 'raw_counts_%s'
% iPolarization['channel']}})
#####################################################################
# 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)
SAR_look_direction = initial_bearing(lon[:, :-1], lat[:, :-1],
lon[:, 1:], lat[:, 1:])
# Interpolate to regain lost row
SAR_look_direction = scipy.ndimage.interpolation.zoom(
SAR_look_direction, (1, 11./10.))
# Decompose, to avoid interpolation errors around 0 <-> 360
SAR_look_direction_u = np.sin(np.deg2rad(SAR_look_direction))
SAR_look_direction_v = np.cos(np.deg2rad(SAR_look_direction))
look_u_VRT = VRT(array=SAR_look_direction_u, lat=lat, lon=lon)
look_v_VRT = VRT(array=SAR_look_direction_v, lat=lat, lon=lon)
# Note: If incidence angle and look direction are stored in
# same VRT, access time is about twice as large
incVRT = VRT(array=inc, lat=lat, lon=lon)
lookVRT = VRT(lat=lat, lon=lon)
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.bandVRTs = {'incVRT': incVRT,
'look_u_VRT': look_u_VRT,
'look_v_VRT': look_v_VRT,
'lookVRT': lookVRT}
# Add band to full sized VRT
incFileName = self.bandVRTs['incVRT'].fileName
lookFileName = self.bandVRTs['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': 'SAR_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())
###################################################################
# Add sigma0_VV
###################################################################
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')
# set SADCAT specific metadata
self.dataset.SetMetadataItem('start_date',
(parse(gdalMetadata['MPH_SENSING_START']).
isoformat()))
self.dataset.SetMetadataItem('stop_date',
(parse(gdalMetadata['MPH_SENSING_STOP']).
isoformat()))
self.dataset.SetMetadataItem('sensor', 'ASAR')
self.dataset.SetMetadataItem('satellite', 'Envisat')
self.dataset.SetMetadataItem('mapper', 'asar')
def __init__(self, fileName, gdalDataset, gdalMetadata, **kwargs):
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...
mdsFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist() if 'measurement/s1a' in fn]
calFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'annotation/calibration/calibration-s1a' in fn]
noiseFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'annotation/calibration/noise-s1a' in fn]
annotationFiles = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'annotation/s1a' in fn]
manifestFile = ['/vsizip/%s/%s' % (fileName, fn)
for fn in zz.namelist()
if 'manifest.safe' in fn]
zz.close()
else:
mdsFiles = glob.glob('%s/measurement/s1a*' % fileName)
calFiles = glob.glob('%s/annotation/calibration/calibration-s1a*'
% fileName)
noiseFiles = glob.glob('%s/annotation/calibration/noise-s1a*'
% fileName)
annotationFiles = glob.glob('%s/annotation/s1a*'
% fileName)
manifestFile = glob.glob('%s/manifest.safe' % fileName)
if (not mdsFiles or not calFiles or not noiseFiles or
not annotationFiles or not manifestFile):
raise WrongMapperError
mdsDict = {}
for mds in mdsFiles:
mdsDict[int((os.path.splitext(os.path.basename(mds))[0].
split('-'))[-1:][0])] = mds
calDict = {}
for ff in calFiles:
calDict[int((os.path.splitext(os.path.basename(ff))[0].
split('-'))[-1:][0])] = ff
noiseDict = {}
for ff in noiseFiles:
noiseDict[int((os.path.splitext(os.path.basename(ff))[0].
split('-'))[-1:][0])] = ff
annotationDict = {}
for ff in annotationFiles:
annotationDict[int((os.path.splitext(os.path.basename(ff))[0].
split('-'))[-1:][0])] = ff
manifestXML = self.read_xml(manifestFile[0])
gdalDatasets = {}
for key in mdsDict.keys():
# Open data files
gdalDatasets[key] = gdal.Open(mdsDict[key])
if not gdalDatasets:
raise WrongMapperError('No Sentinel-1 datasets found')
# Check metadata to confirm it is Sentinel-1 L1
for key in gdalDatasets:
metadata = gdalDatasets[key].GetMetadata()
break
if not 'TIFFTAG_IMAGEDESCRIPTION' in metadata.keys():
raise WrongMapperError
if (not 'Sentinel-1' in metadata['TIFFTAG_IMAGEDESCRIPTION']
and not 'L1' in metadata['TIFFTAG_IMAGEDESCRIPTION']):
raise WrongMapperError
warnings.warn('Sentinel-1 level-1 mapper is not yet adapted to '
'complex data. In addition, the band names should be '
'updated for multi-swath data - '
'and there might be other issues.')
# create empty VRT dataset with geolocation only
for key in gdalDatasets:
VRT.__init__(self, gdalDatasets[key])
break
# Read annotation, noise and calibration xml-files
pol = {}
it = 0
for key in annotationDict.keys():
xml = Node.create(self.read_xml(annotationDict[key]))
pol[key] = (xml.node('product').
node('adsHeader')['polarisation'].upper())
it += 1
if it == 1:
# Get incidence angle
pi = xml.node('generalAnnotation').node('productInformation')
self.dataset.SetMetadataItem('ORBIT_DIRECTION',
str(pi['pass']))
# Incidence angles are found in
#<geolocationGrid>
# <geolocationGridPointList count="#">
# <geolocationGridPoint>
geolocationGridPointList = (xml.node('geolocationGrid').
children[0])
X = []
Y = []
lon = []
lat = []
inc = []
ele = []
for gridPoint in geolocationGridPointList.children:
X.append(int(gridPoint['pixel']))
Y.append(int(gridPoint['line']))
lon.append(float(gridPoint['longitude']))
lat.append(float(gridPoint['latitude']))
inc.append(float(gridPoint['incidenceAngle']))
ele.append(float(gridPoint['elevationAngle']))
X = np.unique(X)
Y = np.unique(Y)
lon = np.array(lon).reshape(len(Y), len(X))
lat = np.array(lat).reshape(len(Y), len(X))
inc = np.array(inc).reshape(len(Y), len(X))
ele = np.array(ele).reshape(len(Y), len(X))
incVRT = VRT(array=inc, lat=lat, lon=lon)
eleVRT = VRT(array=ele, lat=lat, lon=lon)
incVRT = incVRT.get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=2)
eleVRT = eleVRT.get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=2)
self.bandVRTs['incVRT'] = incVRT
self.bandVRTs['eleVRT'] = eleVRT
for key in calDict.keys():
xml = self.read_xml(calDict[key])
calibration_LUT_VRTs, longitude, latitude = (
self.get_LUT_VRTs(xml,
'calibrationVectorList',
['sigmaNought', 'betaNought',
'gamma', 'dn']
))
self.bandVRTs['LUT_sigmaNought_VRT_'+pol[key]] = (
calibration_LUT_VRTs['sigmaNought'].
get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=1))
self.bandVRTs['LUT_betaNought_VRT_'+pol[key]] = (
calibration_LUT_VRTs['betaNought'].
get_resized_vrt(self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=1))
self.bandVRTs['LUT_gamma_VRT'] = calibration_LUT_VRTs['gamma']
self.bandVRTs['LUT_dn_VRT'] = calibration_LUT_VRTs['dn']
for key in noiseDict.keys():
xml = self.read_xml(noiseDict[key])
noise_LUT_VRT = self.get_LUT_VRTs(xml, 'noiseVectorList',
['noiseLut'])[0]
self.bandVRTs['LUT_noise_VRT_'+pol[key]] = (
noise_LUT_VRT['noiseLut'].get_resized_vrt(
self.dataset.RasterXSize,
self.dataset.RasterYSize,
eResampleAlg=1))
metaDict = []
bandNumberDict = {}
bnmax = 0
for key in gdalDatasets.keys():
dsPath, dsName = os.path.split(mdsDict[key])
name = 'DN_%s' % pol[key]
# 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[key].GetRasterBand(1)
dtype = band.DataType
metaDict.append({
'src': {
'SourceFilename': mdsDict[key],
'SourceBand': 1,
'DataType': dtype,
},
'dst': {
'name': name,
'SourceTransferType': gdal.GetDataTypeName(dtype),
'dataType': 6,
},
})
# 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
'''
# Get look direction
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(array=look_direction_u,
lat=latitude, lon=longitude)
look_v_VRT = VRT(array=look_direction_v,
lat=latitude, lon=longitude)
lookVRT = VRT(lat=latitude, lon=longitude)
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,
eResampleAlg=1)
# Store VRTs so that they are accessible later
self.bandVRTs['look_u_VRT'] = look_u_VRT
self.bandVRTs['look_v_VRT'] = look_v_VRT
self.bandVRTs['lookVRT'] = lookVRT
metaDict = []
# Add bands to full size VRT
for key in pol:
name = 'LUT_sigmaNought_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': {'SourceFilename':
(self.bandVRTs['LUT_sigmaNought_VRT_' +
pol[key]].fileName),
'SourceBand': 1
},
'dst': {'name': name
}
})
name = 'LUT_noise_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append({
'src': {
'SourceFilename': self.bandVRTs['LUT_noise_VRT_' +
pol[key]].fileName,
'SourceBand': 1
},
'dst': {
'name': name
}
})
name = 'look_direction'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append({
'src': {
'SourceFilename': self.bandVRTs['lookVRT'].fileName,
'SourceBand': 1
},
'dst': {
'wkv': 'sensor_azimuth_angle',
'name': name
}
})
for key in gdalDatasets.keys():
dsPath, dsName = os.path.split(mdsDict[key])
name = 'sigma0_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': [{'SourceFilename': self.fileName,
'SourceBand': bandNumberDict['DN_%s' % pol[key]],
},
{'SourceFilename': (self.bandVRTs['LUT_noise_VRT_%s'
% pol[key]].fileName),
'SourceBand': 1
},
{'SourceFilename':
(self.bandVRTs['LUT_sigmaNought_VRT_%s'
% pol[key]].fileName),
'SourceBand': 1
}
],
'dst': {'wkv': 'surface_backwards_scattering_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Calibration',
'polarization': pol[key],
'suffix': pol[key],
},
})
name = 'beta0_%s' % pol[key]
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
metaDict.append(
{'src': [{'SourceFilename': self.fileName,
'SourceBand': bandNumberDict['DN_%s' % pol[key]]
},
{'SourceFilename': (self.bandVRTs['LUT_noise_VRT_%s'
% pol[key]].fileName),
'SourceBand': 1
},
{'SourceFilename':
(self.bandVRTs['LUT_betaNought_VRT_%s'
% pol[key]].fileName),
'SourceBand': 1
}
],
'dst': {'wkv': 'surface_backwards_brightness_coefficient_of_radar_wave',
'PixelFunctionType': 'Sentinel1Calibration',
'polarization': pol[key],
'suffix': pol[key],
},
})
self._create_bands(metaDict)
# Add incidence angle as band
name = 'incidence_angle'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
src = {'SourceFilename': self.bandVRTs['incVRT'].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.bandVRTs['eleVRT'].fileName,
'SourceBand': 1}
dst = {'wkv': 'angle_of_elevation',
'name': name}
self._create_band(src, dst)
self.dataset.FlushCache()
# Add sigma0_VV
pp = [pol[key] for key in pol]
if 'VV' not in pp and 'HH' in pp:
name = 'sigma0_VV'
bandNumberDict[name] = bnmax+1
bnmax = bandNumberDict[name]
src = [{'SourceFilename': self.fileName,
'SourceBand': bandNumberDict['DN_HH'],
},
{'SourceFilename': (self.bandVRTs['LUT_noise_VRT_HH'].
fileName),
'SourceBand': 1
},
{'SourceFilename': (self.bandVRTs['LUT_sigmaNought_VRT_HH'].
fileName),
'SourceBand': 1,
},
{'SourceFilename': self.bandVRTs['incVRT'].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()
# set time as acquisition start time
n = Node.create(manifestXML)
meta = n.node('metadataSection')
for nn in meta.children:
if nn.getAttribute('ID') == u'acquisitionPeriod':
# set valid time
self.dataset.SetMetadataItem(
'time_coverage_start',
parse((nn.node('metadataWrap').
node('xmlData').
node('safe:acquisitionPeriod')['safe:startTime'])
).isoformat())
self.dataset.SetMetadataItem(
'time_coverage_end',
parse((nn.node('metadataWrap').
node('xmlData').
node('safe:acquisitionPeriod')['safe:stopTime'])
).isoformat())
# Get dictionary describing the instrument and platform according to
# the GCMD keywords
mm = pti.get_gcmd_instrument('sar')
ee = pti.get_gcmd_platform('sentinel-1a')
# 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, **kwargs):
''' Create Radarsat2 VRT '''
fPathName, fExt = os.path.splitext(fileName)
if zipfile.is_zipfile(fileName):
# Open zip file using VSI
fPath, fName = os.path.split(fPathName)
fileName = '/vsizip/%s/%s' % (fileName, fName)
if not 'RS' in fName[0:2]:
raise WrongMapperError('Provided data is not Radarsat-2')
gdalDataset = gdal.Open(fileName)
gdalMetadata = gdalDataset.GetMetadata()
#if it is not RADARSAT-2, return
if (not gdalMetadata or
not 'SATELLITE_IDENTIFIER' in gdalMetadata.keys()):
raise WrongMapperError
elif gdalMetadata['SATELLITE_IDENTIFIER'] != 'RADARSAT-2':
raise WrongMapperError
# read product.xml
productXmlName = os.path.join(fileName, 'product.xml')
productXml = self.read_xml(productXmlName)
# Get additional metadata from product.xml
rs2_0 = Node.create(productXml)
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
VRT.__init__(self, gdalDataset)
#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(array=look_direction_u, lat=lat, lon=lon)
look_v_VRT = VRT(array=look_direction_v, lat=lat, lon=lon)
# Note: If incidence angle and look direction are stored in
# same VRT, access time is about twice as large
lookVRT = VRT(lat=lat, lon=lon)
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.bandVRTs['look_u_VRT'] = look_u_VRT
self.bandVRTs['look_v_VRT'] = look_v_VRT
self.bandVRTs['lookVRT'] = lookVRT
# Add band to full sized VRT
lookFileName = self.bandVRTs['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('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._add_swath_mask_band()
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
# 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
VRT.__init__(self, gdalDataset)
# 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(array=look_direction_u, lat=lat, lon=lon)
look_v_VRT = VRT(array=look_direction_v, lat=lat, lon=lon)
# Note: If incidence angle and look direction are stored in
# same VRT, access time is about twice as large
incVRT = VRT(array=inc, lat=lat, lon=lon)
lookVRT = VRT(lat=lat, lon=lon)
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.bandVRTs = {'incVRT': incVRT,
'look_u_VRT': look_u_VRT,
'look_v_VRT': look_v_VRT,
'lookVRT': lookVRT}
# Add band to full sized VRT
incFileName = self.bandVRTs['incVRT'].fileName
lookFileName = self.bandVRTs['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))
