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implementation of thermal noise removal

LT1AB
Vincent Schut 2020-10-07 10:54:57 +02:00 committed by piyushrpt
parent 9bfa2dd6db
commit b1bbf0f10f
1 changed files with 127 additions and 76 deletions
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115
components/isceobj/Sensor/GRD/Sentinel1.py
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@ -31,6 +31,7 @@
import isce import isce
import xml.etree.ElementTree as ElementTree import xml.etree.ElementTree as ElementTree
from collections import defaultdict
import datetime import datetime
import isceobj import isceobj
from isceobj.Util import Poly1D, Poly2D from isceobj.Util import Poly1D, Poly2D
@ -121,8 +122,9 @@ class Sentinel1(Component):
self.noiseCorrectionApplied = False self.noiseCorrectionApplied = False
self.betaLUT = None self.betaLUT = None
self.noiseLUT = None
self.gr2srLUT = None self.gr2srLUT = None
self.noiseRangeLUT = None
self.noiseAzimuthLUT = None
self._xml_root=None self._xml_root=None
@ -637,24 +639,19 @@ class Sentinel1(Component):
Extract Noise look up table from calibration file. Extract Noise look up table from calibration file.
''' '''
if not self.noiseCorrectionApplied: from scipy.interpolate import interp1d, InterpolatedUnivariateSpline
self.noiseLUT = 0.0
return
from scipy.interpolate import RectBivariateSpline
if self.noiseXml is None: if self.noiseXml is None:
raise Exception('No calibration file provided') raise Exception('No calibration file provided')
if self.noiseXml.startswith('/vsizip'): if '.zip' in self.noiseXml:
import zipfile import zipfile
parts = self.noiseXml.split(os.path.sep) parts = self.noiseXml.split('.zip/')
zipname = os.path.join(*(parts[2:-4])) zipname = parts[0] + '.zip'
fname = os.path.join(*(parts[-4:])) fname = parts[1]
try: try:
with zipfile.ZipFile(zipname, 'r'): with zipfile.ZipFile(zipname, 'r') as zf:
xmlstr = zf.read(fname) xmlstr = zf.read(fname)
except: except:
raise Exception('Could not read noise file: {0}'.format(self.calibrationXml)) raise Exception('Could not read noise file: {0}'.format(self.calibrationXml))
@ -665,33 +662,78 @@ class Sentinel1(Component):
except: except:
raise Exception('Could not read noise file: {0}'.format(self.calibrationXml)) raise Exception('Could not read noise file: {0}'.format(self.calibrationXml))
if float(self.IPFversion) < 2.90:
noise_range_vector_name = "noiseVectorList"
noise_range_lut_name = "noiseLut"
has_azimuth_noise_vectors = False
self.noiseAzimuthLUT = None
else:
noise_range_vector_name = "noiseRangeVectorList"
noise_range_lut_name = "noiseRangeLut"
has_azimuth_noise_vectors = True
print("Extracting noise LUT's...")
_xml_root = ElementTree.fromstring(xmlstr) _xml_root = ElementTree.fromstring(xmlstr)
node = _xml_root.find('noiseVectorList') node = _xml_root.find(noise_range_vector_name)
num = int(node.attrib['count']) num_vectors = int(node.attrib['count'])
lines = [] print("File contains {} range noise vectors.".format(num_vectors))
pixels = []
data = None full_samples_range = np.arange(self.product.numberOfSamples)
noise_range_lut_indices = np.zeros((num_vectors,))
noise_range_lut_values = np.zeros((num_vectors, self.product.numberOfSamples))
for ii, child in enumerate(node.getchildren()): for ii, child in enumerate(node.getchildren()):
print("Processing range noise vector {}/{}".format(ii + 1, num_vectors))
pixnode = child.find('pixel') pixnode = child.find('pixel')
nump = int(pixnode.attrib['count'])
if ii==0: sample_pixels = [float(x) for x in pixnode.text.split()]
data = np.zeros((num,nump))
pixels = [float(x) for x in pixnode.text.split()]
signode = child.find(noise_range_lut_name)
vector = np.asarray([float(x) for x in signode.text.split()])
vector_interpolator = InterpolatedUnivariateSpline(sample_pixels, vector, k=1)
vector_interpolated = vector_interpolator(full_samples_range)
lines.append( int(child.find('line').text)) noise_range_lut_indices[ii] = int(child.find('line').text)
signode = child.find('noiseLut') noise_range_lut_values[ii] = vector_interpolated
data[ii,:] = [float(x) for x in signode.text.split()]
fp.close() self.noiseRangeLUT = interp1d(noise_range_lut_indices, noise_range_lut_values, kind='linear', axis=0, fill_value="extrapolate")
lines = np.array(lines)
pixels = np.array(pixels) if has_azimuth_noise_vectors:
node = _xml_root.find("noiseAzimuthVectorList")
num_vectors = int(node.attrib['count'])
print("File contains {} azimuth noise blocks.".format(num_vectors))
noise_azimuth_lut_indices = defaultdict(list)
noise_azimuth_lut_values = defaultdict(list)
for block_i, child in enumerate(node.getchildren()):
print("Processing azimuth noise vector {}/{}".format(block_i + 1, num_vectors))
linenode = child.find('line')
signode = child.find("noiseAzimuthLut")
block_range_start = int(child.find('firstRangeSample').text)
block_range_end = int(child.find('lastRangeSample').text)
block_azimuth_start = int(child.find('firstAzimuthLine').text)
block_azimuth_end = int(child.find('lastAzimuthLine').text)
block_line_index = [float(x) for x in linenode.text.split()]
block_vector = [float(x) for x in signode.text.split()]
block_line_range = np.arange(block_azimuth_start, block_azimuth_end + 1)
block_vector_interpolator = InterpolatedUnivariateSpline(block_line_index, block_vector, k=1)
for line in block_line_range:
noise_azimuth_lut_indices[line].extend([block_range_start, block_range_end])
noise_azimuth_lut_values[line].extend([block_vector_interpolator(line)] * 2)
self.noiseAzimuthLUT = (noise_azimuth_lut_indices, noise_azimuth_lut_values)
else:
print("File contains no azimuth noise blocks.")
self.noiseLUT = RectBivariateSpline(lines,pixels, data, kx=1,ky=1)
if False: if False:
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@ -711,7 +753,7 @@ class Sentinel1(Component):
except ImportError: except ImportError:
raise Exception('GDAL python bindings not found. Need this for RSAT2/ TandemX / Sentinel1.') raise Exception('GDAL python bindings not found. Need this for RSAT2/ TandemX / Sentinel1.')
from scipy.interpolate import interp2d from scipy.interpolate import interp1d
if parse: if parse:
self.parse() self.parse()
@ -744,6 +786,9 @@ class Sentinel1(Component):
fid = open(self.output, 'wb') fid = open(self.output, 'wb')
pix = np.arange(self.product.numberOfSamples) pix = np.arange(self.product.numberOfSamples)
if self.noiseAzimuthLUT is not None:
noise_azimuth_lut_indices, noise_azimuth_lut_values = self.noiseAzimuthLUT
for ii in range(self.product.numberOfLines//100 + 1): for ii in range(self.product.numberOfLines//100 + 1):
ymin = int(ii*100) ymin = int(ii*100)
ymax = int(np.clip(ymin+100,0, self.product.numberOfLines)) ymax = int(np.clip(ymin+100,0, self.product.numberOfLines))
@ -752,19 +797,25 @@ class Sentinel1(Component):
break break
lin = np.arange(ymin,ymax) lin = np.arange(ymin,ymax)
####Read in one line of data ####Read in one block of data
data = 1.0 * band.ReadAsArray(0, ymin, self.product.numberOfSamples, ymax-ymin) data = 1.0 * band.ReadAsArray(0, ymin, self.product.numberOfSamples, ymax-ymin)
lut = self.betaLUT(lin,pix,grid=True) lut = self.betaLUT(lin,pix,grid=True)
if noiseFactor != 0.0: if noiseFactor != 0.0:
noise = self.noiseLUT(lin,pix,grid=True) noise = self.noiseRangeLUT(lin)
if self.noiseAzimuthLUT is not None:
block_azimuth_noise = np.zeros_like(noise)
for l_i, line in enumerate(lin):
interpolator = interp1d(noise_azimuth_lut_indices[line], noise_azimuth_lut_values[line], kind='previous', fill_value="extrapolate")
block_azimuth_noise[l_i] = interpolator(np.arange(self.product.numberOfSamples))
noise *= block_azimuth_noise
else: else:
noise = 0.0 noise = 0.0
#outdata = data #outdata = data
outdata = data*data/(lut*lut) + noiseFactor * noise/(lut*lut) outdata = np.clip(data*data/(lut*lut) + noiseFactor * noise/(lut*lut), 0, None)
#outdata = 10 * np.log10(outdata) #outdata = 10 * np.log10(outdata)
outdata.astype(np.float32).tofile(fid) outdata.astype(np.float32).tofile(fid)