def linear_to_dB(self):
self.decibel_list = []
for product in self.temporary_data_list:
parameters = snappy.HashMap()
parameters.put('sourceBands', 'Sigma0_VH,Sigma0_VV')
decibel = GPF.createProduct("LinearToFromdB", parameters, product)
self.decibel_list.append(decibel)
self.temporary_data_list=self.decibel_list
print("Linear to/from dB Conversion is done")
return self.decibel_list
def speckle_filter(self):
speckle_filtered_list = []
for product in self.temporary_data_list:
parameters = snappy.HashMap()
parameters.put('filter', 'Refined Lee')
speckle_filtered = snappy.GPF.createProduct('Speckle-Filter', parameters, product)
speckle_filtered_list.append(speckle_filtered)
self.temporary_data_list = speckle_filtered_list
print('Speckle filtering is done!')
return speckle_filtered_list
def thermal_noise_removel(self):
thermal_noise_list = []
for product in self.temporary_data_list:
parameters = snappy.HashMap()
parameters.put('removeThermalNoise', True)
thermal_noise = snappy.GPF.createProduct("ThermalNoiseRemoval", parameters, product)
thermal_noise_list.append(thermal_noise)
self.temporary_data_list = thermal_noise_list
print('Thermal noise removel is done!')
return self.temporary_data_list
def linear_to_db(s1_read):
"""
分贝化
:param s1_read:
:return:
"""
parameters = snappy.HashMap()
s1_read = snappy.GPF.createProduct('LinearToFromdB', parameters, s1_read)
print('LinearTo/FromdB succeed!')
return s1_read
def add_land_cover(s1_read):
"""
使用AddLandCover GPF module自动下载GlobCover
:param s1_read:
:return:
"""
parameters = snappy.HashMap()
parameters.put("landCoverNames", "GlobCover")
mask_with_land_cover = snappy.GPF.createProduct('AddLandCover', parameters,
s1_read)
return mask_with_land_cover
def calibration(self):
calibrated_list=[]
for product in self.temporary_data_list:
parameters = snappy.HashMap()
parameters.put('outputSigmaBand', True)
parameters.put('selectedPolarizations', 'VH,VV')
parameters.put('outputImageScaleInDb', True)
calibrated = snappy.GPF.createProduct("Calibration", parameters, product)
calibrated_list.append(calibrated)
print('Calibration is done!')
self.temporary_data_list = calibrated_list
return calibrated_list
def thermal_noise_removal(s1_read):
"""
热噪声去除
:param s1_read:
:return:
"""
parameters = snappy.HashMap()
parameters.put('removeThermalNoise', True)
s1_read = snappy.GPF.createProduct('ThermalNoiseRemoval', parameters,
s1_read)
print('Thermal Noise Removal succeed!')
return s1_read
def border_noise_removal(s1_read):
"""
GRD border noise removal
:param s1_read:
:return:
"""
parameters = snappy.HashMap()
parameters.put('selectedPolarisations', 'VH,VV')
s1_read = snappy.GPF.createProduct('Remove-GRD-Border-Noise', parameters,
s1_read)
print('GRD border noise removal succeed!')
return s1_read
def land_sea_mask(s1_read):
"""
:param s1_read:
:return:
"""
parameters = snappy.HashMap()
parameters.put('landMask', False)
parameters.put('useSRTM', True)
landSeaMask = snappy.GPF.createProduct('Land-Sea-Mask', parameters,
s1_read)
print('mask the sea')
return landSeaMask
def apply_orbit_file(s1_read):
"""
精密轨道校正
:param s1_read:
:return:
"""
parameters = snappy.HashMap()
parameters.put('orbitType', 'Sentinel Precise (Auto Download)')
parameters.put('polyDegree', '3')
parameters.put('continueOnFail', 'false')
s1_read = snappy.GPF.createProduct('Apply-Orbit-File', parameters, s1_read)
print('Apply Orbit File succeed!')
return s1_read
def speckle_filter(s1_read):
"""
斑点噪声去除
:param s1_read:
:return:
"""
parameters = snappy.HashMap()
# 使用改进型Lee滤波器
parameters.put('filter', 'Refined Lee')
parameters.put('filterSizeX', 5)
parameters.put('filterSizeY', 5)
s1_read = snappy.GPF.createProduct('Speckle-Filter', parameters, s1_read)
print('Speckle Filter succeed!')
return s1_read
def calibration(s1_read):
"""
辐射校正
:param s1_read:
:return:
"""
parameters = snappy.HashMap()
# 以标准后向散射系数sigma0的方式定标
parameters.put('outputSigmaBand', True)
parameters.put('sourceBands', 'Intensity_VH,Intensity_VV')
parameters.put('selectedPolarisations', 'VH,VV')
# 没有分贝化
parameters.put('outputImageScaleInDb', False)
s1_read = snappy.GPF.createProduct('Calibration', parameters, s1_read)
print('Calibration succeed!')
return s1_read
def geo_subset(self):
subset_list=[]
lake_coordinates = os.path.join(self.lake_coordinates, 'aktas')
data = open(lake_coordinates)
data_string = data.read().replace("\n", " ")
data.close()
# snappy part
for product in self.temporary_data_list:
parameters = snappy.HashMap()
parameters.put('copyMetadata', True)
parameters.put('geoRegion', data_string)
subset = snappy.GPF.createProduct("Subset", parameters, product)
subset_list.append(subset)
list(subset.getBandNames())
self.temporary_data_list = subset_list
print('Subsetting is done!')
return subset_list
def subset(s1_read, subsetx, subsety, subsetw, subseth):
"""
裁剪
:param s1_read:
:param subsetx:
:param subsety:
:param subsetw:
:param subseth:
:return:
"""
parameters = snappy.HashMap()
parameters.put('copyMetadata', True)
parameters.put('region',
'%s, %s, %s, %s' % (subsetx, subsety, subsetw, subseth))
s1_read = snappy.GPF.createProduct('Subset', parameters, s1_read)
print('Subset succeed！')
return s1_read
def apply_threshold_to_product(self):
self.temporary_data_binary = []
snappy.GPF.getDefaultInstance().getOperatorSpiRegistry().loadOperatorSpis()
BandDescriptor = jpy.get_type('org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
targetBand = BandDescriptor()
targetBand.name = 'band1'
targetBand.type = 'float32'
targetBand.expression = 'Sigma0_VV_db < -16.5 '
targetBands = jpy.array('org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 1)
targetBands[0] = targetBand
for product in self.temporary_data_list:
parameters = snappy.HashMap()
parameters.put('name', 'Sigma0_VV_db_Threshold')
parameters.put('targetBands', targetBands)
binary = snappy.GPF.createProduct('BandMaths', parameters, product)
self.temporary_data_binary.append(binary)
self.temporary_data_list = self.temporary_data_binary
return self.temporary_data_binary
def generate_binary_water(s1_read):
"""
依靠GlobCover的分类来生成水体
:param s1_read:
:return:
"""
BandDescriptor = snappy.jpy.get_type(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor')
parameters = snappy.HashMap()
targetBand = BandDescriptor()
targetBand.name = 'BinaryWater'
targetBand.type = 'uint8'
targetBand.expression = '(land_cover_GlobCover == 210) ? 0 : 1'
targetBands = snappy.jpy.array(
'org.esa.snap.core.gpf.common.BandMathsOp$BandDescriptor', 1)
targetBands[0] = targetBand
parameters.put('targetBands', targetBands)
water_mask = snappy.GPF.createProduct('BandMaths', parameters, s1_read)
print('get GlobCover water mask!')
return water_mask
def terrain_correction(s1_read, demFile):
"""
地形校正
:param s1_read:
:param demFile:
:return:
"""
# 地形校正需要获取投影参数WKT文本表示
proj = """GEOGCS["WGS84(DD)",
DATUM["WGS84",
SPHEROID["WGS84", 6378137.0, 298.257223563]],
PRIMEM["Greenwich", 0.0],
UNIT["degree", 0.017453292519943295],
AXIS["Geodetic longitude", EAST],
AXIS["Geodetic latitude", NORTH]]"""
parameters = snappy.HashMap()
if demFile:
parameters.put('deName', 'External DEM')
parameters.put('externalDEMFile', demFile)
parameters.put('externalDEMNoDataValue', 0.0)
print('using external DEM file')
else:
# 使用‘SRTM 3Sec’DEM
parameters.put('deName', 'SRTM 3Sec')
print('using SRTM 3Sec DEM')
parameters.put('imgResamplingMethod', 'BILINEAR_INTERPOLATION')
# 设置分辨率
parameters.put('pixelSpacingInMeter', 10.0)
parameters.put('mapProjection', proj)
# 对海域无高程部分不使用掩膜
parameters.put('nodataValueAtSea', False)
parameters.put('saveSelectedSourceBand', True)
s1_read = snappy.GPF.createProduct('Terrain-Correction', parameters,
s1_read)
print('Terrain Correction succeed!')
return s1_read
subprocess.Popen(['gpt', '-h', 'Write'],
stdout=subprocess.PIPE,
universal_newlines=True).communicate()[0])
path = r'F:\Studium_Trier\Masterarbeit\Datensaetze\atmoCorrected'
images = [
os.path.join(path, f) for f in os.listdir(path) if f.endswith(".SAFE")
]
print(len(images))
for i, item in enumerate(images):
# read products
inputS2 = snappy.ProductIO.readProduct(item)
# resample to 10 m resolution
params = snappy.HashMap()
params.put('referenceBand', 'B2')
params.put('upsampling', 'Nearest')
resample = snappy.GPF.createProduct('Resample', params, inputS2)
# subset to given bands and extent of NHH
wkt = "POLYGON ((6.96598264600004 49.5994875390001,6.96598264600004 49.798110884,7.29804284600004 49.798110884,7.29804284600004 49.5994875390001,6.96598264600004 49.5994875390001))"
bands = "B2,B3,B4,B5,B6,B7,B8,B8A,B11,B12,quality_cloud_confidence,quality_scene_classification"
parameters = snappy.HashMap()
parameters.put('copyMetadata', True)
parameters.put('geoRegion', wkt)
parameters.put('sourceBands', bands)
subset = snappy.GPF.createProduct('Subset', parameters, resample)
# write GeoTIFF
# ToDo: include check if file already exists
def terrain_correction(self):
proj_wgs84 = '''GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.01745329251994328,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4326"]]'''
proj_wgs84_36n = '''PROJCS["WGS 84 / UTM zone 36N",
GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.0174532925199433,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4326"]],
PROJECTION["Transverse_Mercator"],
PARAMETER["latitude_of_origin",0],
PARAMETER["central_meridian",33],
PARAMETER["scale_factor",0.9996],
PARAMETER["false_easting",500000],
PARAMETER["false_northing",0],
UNIT["metre",1,
AUTHORITY["EPSG","9001"]],
AXIS["Easting",EAST],
AXIS["Northing",NORTH],
AUTHORITY["EPSG","32636"]]'''
proj_wgs84_35n = '''PROJCS["WGS 84 / UTM zone 35N",
GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.0174532925199433,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4326"]],
PROJECTION["Transverse_Mercator"],
PARAMETER["latitude_of_origin",0],
PARAMETER["central_meridian",27],
PARAMETER["scale_factor",0.9996],
PARAMETER["false_easting",500000],
PARAMETER["false_northing",0],
UNIT["metre",1,
AUTHORITY["EPSG","9001"]],
AXIS["Easting",EAST],
AXIS["Northing",NORTH],
AUTHORITY["EPSG","32635"]]'''
proj_wgs84_32n = '''PROJCS["WGS 84 / UTM zone 32N",
GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.01745329251994328,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4326"]],
UNIT["metre",1,
AUTHORITY["EPSG","9001"]],
PROJECTION["Transverse_Mercator"],
PARAMETER["latitude_of_origin",0],
PARAMETER["central_meridian",9],
PARAMETER["scale_factor",0.9996],
PARAMETER["false_easting",500000],
PARAMETER["false_northing",0],
AUTHORITY["EPSG","32632"],
AXIS["Easting",EAST],
AXIS["Northing",NORTH]]'''
terrain_corrected_list = []
for product in self.temporary_data_list:
parameters = snappy.HashMap()
parameters.put('demName', 'SRTM 1Sec HGT')
parameters.put('imgResamplingMethod', 'BILINEAR_INTERPOLATION')
parameters.put('pixelSpacingInMeter', 10.0)
parameters.put('mapProjection', proj_wgs84_35n)
parameters.put('nodataValueAtSea', False)
parameters.put('saveSelectedSourceBand', True)
terrain_corrected = snappy.GPF.createProduct('Terrain-Correction', parameters, product)
terrain_corrected_list.append(terrain_corrected)
print('Terrain correction is done!')
self.temporary_data_list = terrain_corrected_list
return self.temporary_data_list
subsets = [] #keeps the list of all subsets objects
for product in products:
subset = GPF.createProduct('Subset', sub_parameters, product)
subsets.append(subset)
print("output_bands:",list(subset.getBandNames()))
# write file in linear units
splits = product.getName().split("_")
outsub = splits[0] + '_' +splits[4][:7] +'_sub'
# snappy.ProductIO.writeProduct(subset, os.path.join(outpath, outsub), 'GeoTIFF') #iskljuci
#********** 4 Apply orbit file *********************
#****************************************************
aof_parameters = snappy.HashMap()
aof_parameters.put('Apply-Orbit-File', True)
aofs=[]
for sub in subsets:
apply_orbit = snappy.GPF.createProduct('Apply-Orbit-File',aof_parameters,sub)
aofs.append(apply_orbit)
print("output_bands:",list(apply_orbit.getBandNames()))
#****** 5. Thermal Noise Removeal ************************
#**************************************************************
tnr = [] #bucket to store objects of TNR
TNR_parameters = snappy.HashMap()
TNR_parameters.put('removeThermalNoise', True)
for aof in aofs:
thermal_noise = snappy.GPF.createProduct('ThermalNoiseRemoval',TNR_parameters,aof)
