def from_lonlat(cls, lon, lat, add_gcps=True):
"""Create Domain object from input longitudes, latitudes arrays
Parameters
----------
lon : numpy.ndarray
longitudes
lat : numpy.ndarray
latitudes
add_gcps : bool
Add GCPs from lon/lat arrays.
Returns
-------
d : Domain
Examples
--------
>>> lon, lat = np.meshgrid(range(10), range(10))
>>> d1 = Domain.from_lonlat(lon, lat)
>>> d2 = Domain.from_lonlat(lon, lat, add_gcps=False) # add only geolocation arrays
"""
d = cls.__new__(cls)
d.vrt = VRT.from_lonlat(lon, lat, add_gcps)
return d
def test_get_super_vrt_geolocation(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt1 = VRT.from_lonlat(lon, lat)
vrt2 = vrt1.get_super_vrt()
vrt1 = None
self.assertTrue(vrt2.geolocation.x_vrt is not None)
self.assertTrue(vrt2.geolocation.y_vrt is not None)
def test_get_super_vrt_geolocation(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt1 = VRT.from_lonlat(lon, lat)
vrt2 = vrt1.get_super_vrt()
vrt1 = None
self.assertTrue(vrt2.geolocation.x_vrt is not None)
self.assertTrue(vrt2.geolocation.y_vrt is not None)
def test_set_gcps_geolocation_geotransform_with_geolocation(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat)
vrt.create_band({str('SourceFilename'): vrt.geolocation.x_vrt.filename})
vrt._set_gcps_geolocation_geotransform()
self.assertFalse('<GeoTransform>' in vrt.xml)
self.assertEqual(vrt.dataset.GetGCPs(), ())
def from_lonlat(cls, lon, lat, add_gcps=True):
"""Create Domain object from input longitudes, latitudes arrays
Parameters
----------
lon : numpy.ndarray
longitudes
lat : numpy.ndarray
latitudes
add_gcps : bool
Add GCPs from lon/lat arrays.
Returns
-------
d : Domain
Examples
--------
>>> lon, lat = np.meshgrid(range(10), range(10))
>>> d1 = Domain.from_lonlat(lon, lat)
>>> d2 = Domain.from_lonlat(lon, lat, add_gcps=False) # add only geolocation arrays
"""
d = cls.__new__(cls)
d.vrt = VRT.from_lonlat(lon, lat, add_gcps)
return d
def test_set_gcps_geolocation_geotransform_with_geolocation(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat)
vrt.create_band({str('SourceFilename'): vrt.geolocation.x_vrt.filename})
vrt._set_gcps_geolocation_geotransform()
self.assertFalse('<GeoTransform>' in vrt.xml)
self.assertEqual(vrt.dataset.GetGCPs(), ())
def test_set_geotransform_for_resize(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat)
vrt._set_geotransform_for_resize()
self.assertEqual(vrt.dataset.GetMetadata(str('GEOLOCATION')), {})
self.assertEqual(vrt.dataset.GetGCPs(), ())
self.assertEqual(vrt.dataset.GetGeoTransform(), (0.0, 1.0, 0.0, 30, 0.0, -1.0))
def test_get_shifted_vrt(self):
deg = 10
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt1 = VRT.from_lonlat(lon, lat)
vrt1.create_geolocation_bands()
vrt2 = vrt1.get_shifted_vrt(deg)
self.assertEqual(vrt1.dataset.GetGeoTransform()[0] + deg,
vrt2.dataset.GetGeoTransform()[0])
def test_set_geotransform_for_resize(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat)
vrt._set_geotransform_for_resize()
self.assertEqual(vrt.dataset.GetMetadata(str('GEOLOCATION')), {})
self.assertEqual(vrt.dataset.GetGCPs(), ())
self.assertEqual(vrt.dataset.GetGeoTransform(), (0.0, 1.0, 0.0, 30, 0.0, -1.0))
def __init__(self, srs=None, ext=None, ds=None, **kwargs):
"""Create Domain from GDALDataset or string options or lat/lon grids"""
# If too much information is given raise error
if ds is not None and srs is not None and ext is not None:
raise ValueError(
'Ambiguous specification of both dataset, srs- and ext-strings.'
)
# choose between input opitons:
# ds
# ds and srs
# srs and ext
# if only a dataset is given:
# copy geo-reference from the dataset
if ds is not None and srs is None:
self.vrt = VRT.from_gdal_dataset(ds)
# If dataset and srs are given (but not ext):
# use AutoCreateWarpedVRT to determine bounds and resolution
elif ds is not None and srs is not None:
srs = NSR(srs)
tmp_vrt = gdal.AutoCreateWarpedVRT(ds, None, srs.wkt)
if tmp_vrt is None:
raise NansatProjectionError(
'Could not warp the given dataset to the given SRS.')
else:
self.vrt = VRT.from_gdal_dataset(tmp_vrt)
# If SpatialRef and extent string are given (but not dataset)
elif srs is not None and ext is not None:
srs = NSR(srs)
# create full dictionary of parameters
extent_dict = Domain._create_extent_dict(ext)
# convert -lle to -te
if 'lle' in extent_dict.keys():
extent_dict = self._convert_extentDic(srs, extent_dict)
# get size/extent from the created extent dictionary
geo_transform, raster_x_size, raster_y_size = self._get_geotransform(
extent_dict)
# create VRT object with given geo-reference parameters
self.vrt = VRT.from_dataset_params(x_size=raster_x_size,
y_size=raster_y_size,
geo_transform=geo_transform,
projection=srs.wkt,
gcps=[],
gcp_projection='')
elif 'lat' in kwargs and 'lon' in kwargs:
warnings.warn(
'Domain(lon=lon, lat=lat) will be deprectaed!'
'Use Domain.from_lonlat()', NansatFutureWarning)
# create self.vrt from given lat/lon
self.vrt = VRT.from_lonlat(kwargs['lon'], kwargs['lat'])
else:
raise ValueError('"dataset" or "srsString and extentString" '
'or "dataset and srsString" are required')
def test_reproject_gcps(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt1 = VRT.from_lonlat(lon, lat)
vrt1.reproject_gcps(str('+proj=cea'))
self.assertIn('Cylindrical_Equal_Area', vrt1.dataset.GetGCPProjection())
self.assertEqual(vrt1.dataset.GetGCPs()[0].GCPX, 0)
self.assertEqual(vrt1.dataset.GetGCPs()[0].GCPY, 1100285.5701634109)
self.assertEqual(vrt1.dataset.GetGCPs()[-1].GCPX, 556597.4539663679)
self.assertEqual(vrt1.dataset.GetGCPs()[-1].GCPY, 2096056.5506871857)
def test_reproject_gcps(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt1 = VRT.from_lonlat(lon, lat)
vrt1.reproject_GCPs(str('+proj=stere'))
self.assertIn('Stereographic', vrt1.dataset.GetGCPProjection())
self.assertEqual(vrt1.dataset.GetGCPs()[0].GCPX, 0)
self.assertEqual(vrt1.dataset.GetGCPs()[0].GCPY, 2217341.7476875726)
self.assertEqual(vrt1.dataset.GetGCPs()[-1].GCPX, 1082008.9593705384)
self.assertEqual(vrt1.dataset.GetGCPs()[-1].GCPY, 4320951.334629638)
def test_reproject_gcps(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt1 = VRT.from_lonlat(lon, lat)
vrt1.reproject_gcps(str('+proj=cea'))
self.assertIn('Cylindrical_Equal_Area', vrt1.dataset.GetGCPProjection())
self.assertEqual(round(vrt1.dataset.GetGCPs()[0].GCPX), 0)
self.assertEqual(round(vrt1.dataset.GetGCPs()[0].GCPY), 1100286)
self.assertEqual(round(vrt1.dataset.GetGCPs()[-1].GCPX), 556597)
self.assertEqual(round(vrt1.dataset.GetGCPs()[-1].GCPY), 2096057)
def test_set_gcps_geolocation_geotransform_with_gcps(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat)
vrt.create_band({'SourceFilename': vrt.geolocation.x_vrt.filename})
vrt._remove_geolocation()
vrt._set_gcps_geolocation_geotransform()
self.assertFalse('<GeoTransform>' in vrt.xml)
self.assertIsInstance(vrt.dataset.GetGCPs(), (list, tuple))
self.assertTrue(len(vrt.dataset.GetGCPs()) > 0)
self.assertEqual(vrt.dataset.GetMetadata(str('GEOLOCATION')), {})
def test_create_geolocation_bands(self):
lon, lat = np.meshgrid(np.linspace(0,5,10), np.linspace(10,20,30))
vrt = VRT.from_lonlat(lon, lat)
vrt.create_geolocation_bands()
self.assertEqual(vrt.dataset.RasterCount, 2)
self.assertEqual(vrt.dataset.GetRasterBand(1).GetMetadataItem(str('name')), 'longitude')
self.assertEqual(vrt.dataset.GetRasterBand(2).GetMetadataItem(str('name')), 'latitude')
self.assertTrue(np.allclose(vrt.dataset.GetRasterBand(1).ReadAsArray(), lon))
self.assertTrue(np.allclose(vrt.dataset.GetRasterBand(2).ReadAsArray(), lat))
def test_set_gcps_geolocation_geotransform_with_gcps(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat)
vrt.create_band({'SourceFilename': vrt.geolocation.x_vrt.filename})
vrt._remove_geolocation()
vrt._set_gcps_geolocation_geotransform()
self.assertFalse('<GeoTransform>' in vrt.xml)
self.assertIsInstance(vrt.dataset.GetGCPs(), (list, tuple))
self.assertTrue(len(vrt.dataset.GetGCPs()) > 0)
self.assertEqual(vrt.dataset.GetMetadata(str('GEOLOCATION')), {})
def test_create_geolocation_bands(self):
lon, lat = np.meshgrid(np.linspace(0,5,10), np.linspace(10,20,30))
vrt = VRT.from_lonlat(lon, lat)
vrt.create_geolocation_bands()
self.assertEqual(vrt.dataset.RasterCount, 2)
self.assertEqual(vrt.dataset.GetRasterBand(1).GetMetadataItem(str('name')), 'longitude')
self.assertEqual(vrt.dataset.GetRasterBand(2).GetMetadataItem(str('name')), 'latitude')
self.assertTrue(np.allclose(vrt.dataset.GetRasterBand(1).ReadAsArray(), lon))
self.assertTrue(np.allclose(vrt.dataset.GetRasterBand(2).ReadAsArray(), lat))
def __init__(self, srs=None, ext=None, ds=None, **kwargs):
"""Create Domain from GDALDataset or string options or lat/lon grids"""
# If too much information is given raise error
if ds is not None and srs is not None and ext is not None:
raise ValueError('Ambiguous specification of both dataset, srs- and ext-strings.')
# choose between input opitons:
# ds
# ds and srs
# srs and ext
# if only a dataset is given:
# copy geo-reference from the dataset
if ds is not None and srs is None:
self.vrt = VRT.from_gdal_dataset(ds)
# If dataset and srs are given (but not ext):
# use AutoCreateWarpedVRT to determine bounds and resolution
elif ds is not None and srs is not None:
srs = NSR(srs)
tmp_vrt = gdal.AutoCreateWarpedVRT(ds, None, srs.wkt)
if tmp_vrt is None:
raise NansatProjectionError('Could not warp the given dataset to the given SRS.')
else:
self.vrt = VRT.from_gdal_dataset(tmp_vrt)
# If SpatialRef and extent string are given (but not dataset)
elif srs is not None and ext is not None:
srs = NSR(srs)
# create full dictionary of parameters
extent_dict = Domain._create_extent_dict(ext)
# convert -lle to -te
if 'lle' in extent_dict.keys():
extent_dict = self._convert_extentDic(srs, extent_dict)
# get size/extent from the created extent dictionary
geo_transform, raster_x_size, raster_y_size = self._get_geotransform(extent_dict)
# create VRT object with given geo-reference parameters
self.vrt = VRT.from_dataset_params(x_size=raster_x_size, y_size=raster_y_size,
geo_transform=geo_transform,
projection=srs.wkt,
gcps=[], gcp_projection='')
elif 'lat' in kwargs and 'lon' in kwargs:
warnings.warn('Domain(lon=lon, lat=lat) will be deprectaed!'
'Use Domain.from_lonlat()', NansatFutureWarning)
# create self.vrt from given lat/lon
self.vrt = VRT.from_lonlat(kwargs['lon'], kwargs['lat'])
else:
raise ValueError('"dataset" or "srsString and extentString" '
'or "dataset and srsString" are required')
def test_from_lonlat(self):
geo_keys = ['LINE_OFFSET', 'LINE_STEP', 'PIXEL_OFFSET', 'PIXEL_STEP', 'SRS',
'X_BAND', 'X_DATASET', 'Y_BAND', 'Y_DATASET']
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat, n_gcps=25)
self.assertEqual(vrt.dataset.RasterXSize, 10)
self.assertEqual(vrt.dataset.RasterYSize, 30)
self.assertIn('filename', list(vrt.dataset.GetMetadata().keys()))
geo_metadata = vrt.dataset.GetMetadata(str('GEOLOCATION'))
for geo_key in geo_keys:
self.assertEqual(vrt.geolocation.data[geo_key], geo_metadata[geo_key])
self.assertIsInstance(vrt.geolocation.x_vrt, VRT)
self.assertIsInstance(vrt.geolocation.y_vrt, VRT)
self.assertEqual(vrt.geolocation.x_vrt.filename, geo_metadata['X_DATASET'])
self.assertEqual(vrt.geolocation.y_vrt.filename, geo_metadata['Y_DATASET'])
self.assertEqual(len(vrt.dataset.GetGCPs()), 25)
def test_from_lonlat(self):
geo_keys = ['LINE_OFFSET', 'LINE_STEP', 'PIXEL_OFFSET', 'PIXEL_STEP', 'SRS',
'X_BAND', 'X_DATASET', 'Y_BAND', 'Y_DATASET']
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat, n_gcps=25)
self.assertEqual(vrt.dataset.RasterXSize, 10)
self.assertEqual(vrt.dataset.RasterYSize, 30)
self.assertIn('filename', list(vrt.dataset.GetMetadata().keys()))
geo_metadata = vrt.dataset.GetMetadata(str('GEOLOCATION'))
for geo_key in geo_keys:
self.assertEqual(vrt.geolocation.data[geo_key], geo_metadata[geo_key])
self.assertIsInstance(vrt.geolocation.x_vrt, VRT)
self.assertIsInstance(vrt.geolocation.y_vrt, VRT)
self.assertEqual(vrt.geolocation.x_vrt.filename, geo_metadata['X_DATASET'])
self.assertEqual(vrt.geolocation.y_vrt.filename, geo_metadata['Y_DATASET'])
self.assertEqual(len(vrt.dataset.GetGCPs()), 25)
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 test_super_vrt_of_geolocation_bands(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt1 = VRT.from_lonlat(lon, lat)
vrt1.create_geolocation_bands()
vrt2 = vrt1.get_super_vrt()
self.assertTrue(hasattr(vrt2.vrt, 'vrt'))
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 test_super_vrt_of_geolocation_bands(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt1 = VRT.from_lonlat(lon, lat)
vrt1.create_geolocation_bands()
vrt2 = vrt1.get_super_vrt()
self.assertTrue(hasattr(vrt2.vrt, 'vrt'))
def test_from_lonlat_no_gcps(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat, add_gcps=False)
self.assertEqual(len(vrt.dataset.GetGCPs()), 0)
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)
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)
#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('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))
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 test_from_lonlat_no_gcps(self):
lon, lat = np.meshgrid(np.linspace(0, 5, 10), np.linspace(10, 20, 30))
vrt = VRT.from_lonlat(lon, lat, add_gcps=False)
self.assertEqual(len(vrt.dataset.GetGCPs()), 0)
def __init__(self,
srs=None,
ext=None,
ds=None,
lon=None,
lat=None,
name='',
logLevel=None):
"""Create Domain from GDALDataset or string options or lat/lon grids"""
# set default attributes
self.logger = add_logger('Nansat', logLevel)
self.name = name
self.logger.debug('ds: %s' % str(ds))
self.logger.debug('srs: %s' % srs)
self.logger.debug('ext: %s' % ext)
# If too much information is given raise error
if ds is not None and srs is not None and ext is not None:
raise ValueError(
'Ambiguous specification of both dataset, srs- and ext-strings.'
)
# choose between input opitons:
# ds
# ds and srs
# srs and ext
# lon and lat
# if only a dataset is given:
# copy geo-reference from the dataset
if ds is not None and srs is None:
self.vrt = VRT.from_gdal_dataset(ds)
# If dataset and srs are given (but not ext):
# use AutoCreateWarpedVRT to determine bounds and resolution
elif ds is not None and srs is not None:
srs = NSR(srs)
tmp_vrt = gdal.AutoCreateWarpedVRT(ds, None, srs.wkt)
if tmp_vrt is None:
raise NansatProjectionError(
'Could not warp the given dataset to the given SRS.')
else:
self.vrt = VRT.from_gdal_dataset(tmp_vrt)
# If SpatialRef and extent string are given (but not dataset)
elif srs is not None and ext is not None:
srs = NSR(srs)
# create full dictionary of parameters
extent_dict = Domain._create_extent_dict(ext)
# convert -lle to -te
if 'lle' in extent_dict.keys():
extent_dict = self._convert_extentDic(srs, extent_dict)
# get size/extent from the created extent dictionary
geo_transform, raster_x_size, raster_y_size = self._get_geotransform(
extent_dict)
# create VRT object with given geo-reference parameters
self.vrt = VRT.from_dataset_params(x_size=raster_x_size,
y_size=raster_y_size,
geo_transform=geo_transform,
projection=srs.wkt,
gcps=[],
gcp_projection='')
elif lat is not None and lon is not None:
# create self.vrt from given lat/lon
self.vrt = VRT.from_lonlat(lon, lat)
else:
raise ValueError('"dataset" or "srsString and extentString" '
'or "dataset and srsString" are required')
self.logger.debug('vrt.dataset: %s' % str(self.vrt.dataset))
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,
**kwargs):
if kwargs.get('manifestonly', False):
fast = True
NansatFutureWarning(
'manifestonly option will be deprecated. Use: fast=True')
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
annotation_data = self.read_annotation(annotation_files)
if not fast:
annotation_data = Mapper.correct_geolocation_data(annotation_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, annotation_data)
# 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(
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 __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)
# 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()