def run(self):
ndv = NDV
nbar = self.tile.datasets[DatasetType.ARG25]
_log.info("Processing tile [%s]", nbar.path)
# Apply PQA if specified
pqa = None
if self.mask_pqa_apply and DatasetType.PQ25 in self.tile.datasets:
pqa = self.tile.datasets[DatasetType.PQ25]
mask = None
log_mem("Before get PQA mask")
if pqa:
mask = get_mask_pqa(pqa, self.mask_pqa_mask)
data = get_dataset_data_masked(nbar, mask=mask, ndv=ndv)
log_mem("After get data (masked)")
metadata = get_dataset_metadata(nbar)
data = calculate_tassel_cap_index(data, coefficients=TCI_COEFFICIENTS[nbar.satellite][TasselCapIndex.WETNESS])
raster_create(self.output().path, [data], metadata.transform, metadata.projection, numpy.nan, gdal.GDT_Float32)
def create_water_tile(self, tile):
arg = tile.datasets[DatasetType.ARG25]
pqa = tile.datasets[DatasetType.PQ25]
_log.info("ARG tile [%s]", arg)
_log.info("PQ tile [%s]", pqa)
filename = os.path.basename(arg.path)
filename = filename.replace("NBAR", "WETNESS")
filename = filename.replace(".vrt", ".tif")
filename = os.path.join(self.output_directory, filename)
metadata = get_dataset_metadata(arg)
data = get_dataset_data_with_pq(arg, Ls57Arg25Bands, pqa)
# Calculate TCI Wetness
tci = calculate_tassel_cap_index(data,
coefficients=TCI_COEFFICIENTS[arg.satellite][TasselCapIndex.WETNESS])
_log.info("TCI shape is %s | min = %s | max = %s", numpy.shape(tci), tci.min(), tci.max())
raster_create(filename,
[tci],
metadata.transform, metadata.projection, numpy.nan, gdal.GDT_Float32)
def classifier(arg25_dataset, pq25_dataset):
"""
Runs the classifier designed by SF.
"""
# Get the metadata
md = get_dataset_metadata(arg25_dataset)
cols, rows = md.shape
# Read the data and mask pixels via the PQ dataset
data = get_dataset_data_with_pq(arg25_dataset, pq25_dataset)
# Get the wetness coefficients and calculate
coef = TCI_COEFFICIENTS[arg25_dataset.satellite][TasselCapIndex.WETNESS]
wetness = calculate_tassel_cap_index(data, coef)
# NDVI
ndvi = calculate_ndvi(data[arg25_dataset.bands.RED],
data[arg25_dataset.bands.NEAR_INFRARED],
output_ndv=numpy.nan)
# Dump the reflectance data, the classifier only needs tc_wetness and ndvi
del data
# Allocate the result
classified = numpy.zeros((rows, cols), dtype='uint8')
# Water
r1 = wetness > 0
classified[r1] = 1
_tmp = ~r1
#r2 = _tmp & ((wetness >= -250) & (wetness < 0))
r2 = (wetness >= -250) & (wetness < 0)
r3 = ndvi <= 0.3
#_tmp2 = _tmp & r2 & ~r3
_tmp2 = _tmp & r2
# non-veg
classified[_tmp2 & r3] = 2
_tmp3 = _tmp2 & ~r3
r4 = ndvi <= 0.45
# saltmarsh
classified[_tmp3 & r4] = 3
_tmp2 = _tmp3 & ~r4
r5 = ndvi <= 0.6
# mangrove/saltmarsh
classified[_tmp2 & r5] = 4
# mangrove
classified[_tmp2 & ~r5] = 5
# finished rhs of r2
_tmp2 = _tmp & ~r2
r6 = wetness < -750
r7 = ndvi >= 0.3
_tmp3 = _tmp2 & r6
# saltmarsh
classified[_tmp3 & r7] = 3
# non-veg
classified[_tmp3 & ~r7] = 2
r8 = ndvi <= 0.3
_tmp3 = _tmp2 & ~r6
# non-veg
classified[_tmp3 & r8] = 2
r9 = ndvi <= 0.45
_tmp2 = _tmp3 & ~r8
# saltmarsh
classified[_tmp2 & r9] = 3
r10 = ndvi <= 0.6
_tmp3 = _tmp2 & ~r9
# mangrove-saltmarsh
classified[_tmp3 & r10] = 4
# mangrove
classified[_tmp3 & ~r10] = 5
# set any nulls
valid = numpy.isfinite(ndvi)
classified[~valid] = 0
return classified
def classifier(arg25_dataset, pq25_dataset):
"""
Runs the classifier designed by SF.
"""
# Get the metadata
md = get_dataset_metadata(arg25_dataset)
cols, rows = md.shape
# Read the data and mask pixels via the PQ dataset
data = get_dataset_data_with_pq(arg25_dataset, pq25_dataset)
# Get the wetness coefficients and calculate
coef = TCI_COEFFICIENTS[arg25_dataset.satellite][TasselCapIndex.WETNESS]
wetness = calculate_tassel_cap_index(data, coef)
# NDVI
ndvi = calculate_ndvi(data[arg25_dataset.bands.RED],
data[arg25_dataset.bands.NEAR_INFRARED],
output_ndv=numpy.nan)
# Dump the reflectance data, the classifier only needs tc_wetness and ndvi
del data
# Allocate the result
classified = numpy.zeros((rows,cols), dtype='uint8')
# Water
r1 = wetness > 0
classified[r1] = 1
_tmp = ~r1
#r2 = _tmp & ((wetness >= -250) & (wetness < 0))
r2 = (wetness >= -250) & (wetness < 0)
r3 = ndvi <= 0.3
#_tmp2 = _tmp & r2 & ~r3
_tmp2 = _tmp & r2
# non-veg
classified[_tmp2 & r3] = 2
_tmp3 = _tmp2 & ~r3
r4 = ndvi <= 0.45
# saltmarsh
classified[_tmp3 & r4] = 3
_tmp2 = _tmp3 & ~r4
r5 = ndvi <= 0.6
# mangrove/saltmarsh
classified[_tmp2 & r5] = 4
# mangrove
classified[_tmp2 & ~r5] = 5
# finished rhs of r2
_tmp2 = _tmp & ~r2
r6 = wetness < -750
r7 = ndvi >= 0.3
_tmp3 = _tmp2 & r6
# saltmarsh
classified[_tmp3 & r7] = 3
# non-veg
classified[_tmp3 & ~r7] = 2
r8 = ndvi <= 0.3
_tmp3 = _tmp2 & ~r6
# non-veg
classified[_tmp3 & r8] = 2
r9 = ndvi <= 0.45
_tmp2 = _tmp3 & ~r8
# saltmarsh
classified[_tmp2 & r9] = 3
r10 = ndvi <= 0.6
_tmp3 = _tmp2 & ~r9
# mangrove-saltmarsh
classified[_tmp3 & r10] = 4
# mangrove
classified[_tmp3 & ~r10] = 5
# set any nulls
valid = numpy.isfinite(ndvi)
classified[~valid] = 0
return classified