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Merge pull request #95 from vbrancat/master 

Modified Ionospheric Phase correction
LT1AB
Heresh Fattahi 2020-03-01 10:49:49 -08:00 committed by GitHub
parent f864b582a4 9f9f7be0d7
commit f31550bd0c
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 132 additions and 134 deletions
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9
applications/stripmapApp.py
View File

@ -384,6 +384,13 @@ RENDERER = Application.Parameter(
)
)
DISPERSIVE_FILTER_FILLING_METHOD = Application.Parameter('dispersive_filling_method',
public_name = 'dispersive filter filling method',
default='nearest_neighbour',
type=str,
mandatory=False,
doc='method to fill the holes left by masking the ionospheric phase estimate')
DISPERSIVE_FILTER_KERNEL_XSIZE = Application.Parameter('kernel_x_size',
public_name='dispersive filter kernel x-size',
default=800,
@ -439,7 +446,6 @@ DISPERSIVE_FILTER_COHERENCE_THRESHOLD = Application.Parameter('dispersive_filter
type=float,
mandatory=False,
doc='Coherence threshold to generate a mask file which gets used in the iterative filtering of the dispersive and non-disperive phase')
#Facility declarations
MASTER = Application.Facility(
@ -548,6 +554,7 @@ class _RoiBase(Application, FrameMixin):
PICKLE_LOAD_DIR,
RENDERER,
DO_DISPERSIVE,
DISPERSIVE_FILTER_FILLING_METHOD,
DISPERSIVE_FILTER_KERNEL_XSIZE,
DISPERSIVE_FILTER_KERNEL_YSIZE,
DISPERSIVE_FILTER_KERNEL_SIGMA_X,

251
components/isceobj/StripmapProc/runDispersive.py
View File

@ -3,17 +3,12 @@
#
#
import logging
import os
import os,gdal
import isceobj
from isceobj.Constants import SPEED_OF_LIGHT
import numpy as np
import gdal
try:
import cv2
except ImportError:
print('OpenCV2 does not appear to be installed / is not importable.')
print('OpenCV2 is needed for this step. You may experience failures ...')
logger = logging.getLogger('isce.insar.runDispersive')
@ -29,19 +24,8 @@ def getValue(dataFile, band, y_ref, x_ref):
ds = None
return ref[0][0]
def check_consistency(lowBandIgram, highBandIgram, outputDir):
jumpFile = os.path.join(outputDir , "jumps.bil")
cmd = 'imageMath.py -e="round((a_1-b_1)/(2.0*PI))" --a={0} --b={1} -o {2} -t float -s BIL'.format(lowBandIgram, highBandIgram, jumpFile)
print(cmd)
os.system(cmd)
return jumpFile
def dispersive_nonDispersive(lowBandIgram, highBandIgram, f0, fL, fH, outDispersive, outNonDispersive, jumpFile, y_ref=None, x_ref=None, m=None , d=None):
def dispersive_nonDispersive(lowBandIgram, highBandIgram, f0, fL, fH, outDispersive, outNonDispersive, y_ref=None, x_ref=None, m=None , d=None):
if y_ref and x_ref:
refL = getValue(lowBandIgram, 2, y_ref, x_ref)
@ -57,100 +41,80 @@ def dispersive_nonDispersive(lowBandIgram, highBandIgram, f0, fL, fH, outDispers
if m and d:
coef = (fL*fH)/(f0*(fH**2 - fL**2))
#cmd = 'imageMath.py -e="{0}*((a_1-{8}-2*PI*c)*{1}-(b_1-{9}-2*PI*(c+f))*{2})" --a={3} --b={4} --c={5} --f={6} -o {7} -t float32 -s BIL'.format(coef,fH, fL, lowBandIgram, highBandIgram, m , d, outDispersive, refL, refH)
cmd = 'imageMath.py -e="{0}*((a_1-2*PI*c)*{1}-(b_1+(2.0*PI*g)-2*PI*(c+f))*{2})" --a={3} --b={4} --c={5} --f={6} --g={7} -o {8} -t float32 -s BIL'.format(coef,fH, fL, lowBandIgram, highBandIgram, m , d, jumpFile, outDispersive)
cmd = 'imageMath.py -e="{0}*((a_1-2*PI*c)*{1}-(b_1+(2.0*PI)-2*PI*(c+f))*{2})" --a={3} --b={4} --c={5} --f={6} -o {7} -t float32 -s BIL'.format(coef,fH, fL, lowBandIgram, highBandIgram, m , d, outDispersive)
print(cmd)
os.system(cmd)
coefn = f0/(fH**2-fL**2)
#cmd = 'imageMath.py -e="{0}*((a_1-{8}-2*PI*c)*{1}-(b_1-{9}-2*PI*(c+f))*{2})" --a={3} --b={4} --c={5} --f={6} -o {7} -t float32 -s BIL'.format(coefn,fH, fL, highBandIgram, lowBandIgram, m , d, outNonDispersive, refH, refL)
cmd = 'imageMath.py -e="{0}*((a_1+(2.0*PI*g)-2*PI*c)*{1}-(b_1-2*PI*(c+f))*{2})" --a={3} --b={4} --c={5} --f={6} --g={7} -o {8} -t float32 -s BIL'.format(coefn,fH, fL, highBandIgram, lowBandIgram, m , d, jumpFile, outNonDispersive)
cmd = 'imageMath.py -e="{0}*((a_1+(2.0*PI)-2*PI*c)*{1}-(b_1-2*PI*(c+f))*{2})" --a={3} --b={4} --c={5} --f={6} -o {7} -t float32 -s BIL'.format(coefn,fH, fL, highBandIgram, lowBandIgram, m , d, outNonDispersive)
print(cmd)
os.system(cmd)
else:
coef = (fL*fH)/(f0*(fH**2 - fL**2))
#cmd = 'imageMath.py -e="{0}*((a_1-{6})*{1}-(b_1-{7})*{2})" --a={3} --b={4} -o {5} -t float32 -s BIL'.format(coef,fH, fL, lowBandIgram, highBandIgram, outDispersive, refL, refH)
cmd = 'imageMath.py -e="{0}*(a_1*{1}-(b_1+2.0*PI*c)*{2})" --a={3} --b={4} --c={5} -o {6} -t float32 -s BIL'.format(coef,fH, fL, lowBandIgram, highBandIgram, jumpFile, outDispersive)
cmd = 'imageMath.py -e="{0}*(a_1*{1}-(b_1+2.0*PI)*{2})" --a={3} --b={4} -o {5} -t float32 -s BIL'.format(coef,fH, fL, lowBandIgram, highBandIgram, outDispersive)
print(cmd)
os.system(cmd)
coefn = f0/(fH**2-fL**2)
#cmd = 'imageMath.py -e="{0}*((a_1-{6})*{1}-(b_1-{7})*{2})" --a={3} --b={4} -o {5} -t float32 -s BIL'.format(coefn,fH, fL, highBandIgram, lowBandIgram, outNonDispersive, refH, refL)
cmd = 'imageMath.py -e="{0}*((a_1+2.0*PI*c)*{1}-(b_1)*{2})" --a={3} --b={4} --c={5} -o {6} -t float32 -s BIL'.format(coefn,fH, fL, highBandIgram, lowBandIgram, jumpFile, outNonDispersive)
cmd = 'imageMath.py -e="{0}*((a_1+2.0*PI)*{1}-(b_1)*{2})" --a={3} --b={4} -o {5} -t float32 -s BIL'.format(coefn,fH, fL, highBandIgram, lowBandIgram, outNonDispersive)
print(cmd)
os.system(cmd)
return None
def std_iono_mean_coh(f0,fL,fH,coh_mean,rgLooks,azLooks):
# From Liao et al., Remote Sensing of Environment 2018
# STD sub-band at average coherence value (Eq. 8)
Nb = (rgLooks*azLooks)/3.0
coeffA = (np.sqrt(2.0*Nb))**(-1)
coeffB = np.sqrt(1-coh_mean**2)/coh_mean
std_subbands = coeffA * coeffB
# STD Ionosphere (Eq. 7)
coeffC = np.sqrt(1+(fL/fH)**2)
coeffD = (fH*fL*fH)/(f0*(fH**2-fL**2))
std_iono = coeffC*coeffD*std_subbands
return std_iono
def theoretical_variance_fromSubBands(self, f0, fL, fH, B, Sig_phi_iono, Sig_phi_nonDisp,N):
# Calculating the theoretical variance of the
# ionospheric phase based on the coherence of
# the sub-band interferograns
# Calculating the theoretical variance of the ionospheric phase based on the coherence of the sub-band interferograns
ifgDirname = os.path.join(self.insar.ifgDirname, self.insar.lowBandSlcDirname)
lowBandCoherence = os.path.join(ifgDirname , self.insar.coherenceFilename)
Sig_phi_L = os.path.join(ifgDirname , 'filt_' + self.insar.ifgFilename + ".sig")
ifgDirname = os.path.join(self.insar.ifgDirname, self.insar.highBandSlcDirname)
#highBandIgram = os.path.join(ifgDirname , 'filt_' + self.insar.ifgFilename + ".unw")
#ifgDirname = os.path.dirname(self.insar.lowBandIgram)
#lowBandCoherence = os.path.join(ifgDirname , self.insar.coherenceFilename)
#Sig_phi_L = os.path.join(ifgDirname , 'filt_' + self.insar.ifgFilename + ".sig")
#ifgDirname = os.path.dirname(self.insar.highBandIgram)
highBandCoherence = os.path.join(ifgDirname , self.insar.coherenceFilename)
Sig_phi_H = os.path.join(ifgDirname , 'filt_' + self.insar.ifgFilename + ".sig")
#N = self.numberAzimuthLooks*self.numberRangeLooks
#PI = np.pi
#fL,f0,fH,B = getBandFrequencies(inps)
#cL = read(inps.lowBandCoherence,bands=[1])
#cL = cL[0,:,:]
#cL[cL==0.0]=0.001
cmd = 'imageMath.py -e="sqrt(1-a**2)/a/sqrt(2.0*{0})" --a={1} -o {2} -t float -s BIL'.format(N, lowBandCoherence, Sig_phi_L)
print(cmd)
os.system(cmd)
#Sig_phi_L = np.sqrt(1-cL**2)/cL/np.sqrt(2.*N)
#cH = read(inps.highBandCoherence,bands=[1])
#cH = cH[0,:,:]
#cH[cH==0.0]=0.001
cmd = 'imageMath.py -e="sqrt(1-a**2)/a/sqrt(2.0*{0})" --a={1} -o {2} -t float -s BIL'.format(N, highBandCoherence, Sig_phi_H)
print(cmd)
os.system(cmd)
#Sig_phi_H = np.sqrt(1-cH**2)/cH/np.sqrt(2.0*N)
coef = (fL*fH)/(f0*(fH**2 - fL**2))
cmd = 'imageMath.py -e="sqrt(({0}**2)*({1}**2)*(a**2) + ({0}**2)*({2}**2)*(b**2))" --a={3} --b={4} -o {5} -t float -s BIL'.format(coef, fL, fH, Sig_phi_L, Sig_phi_H, Sig_phi_iono)
os.system(cmd)
#Sig_phi_iono = np.sqrt((coef**2)*(fH**2)*Sig_phi_H**2 + (coef**2)*(fL**2)*Sig_phi_L**2)
#length, width = Sig_phi_iono.shape
#outFileIono = os.path.join(inps.outDir, 'Sig_iono.bil')
#write(Sig_phi_iono, outFileIono, 1, 6)
#write_xml(outFileIono, length, width)
coef_non = f0/(fH**2 - fL**2)
cmd = 'imageMath.py -e="sqrt(({0}**2)*({1}**2)*(a**2) + ({0}**2)*({2}**2)*(b**2))" --a={3} --b={4} -o {5} -t float -s BIL'.format(coef_non, fL, fH, Sig_phi_L, Sig_phi_H, Sig_phi_nonDisp)
os.system(cmd)
#Sig_phi_non_dis = np.sqrt((coef_non**2) * (fH**2) * Sig_phi_H**2 + (coef_non**2) * (fL**2) * Sig_phi_L**2)
#outFileNonDis = os.path.join(inps.outDir, 'Sig_nonDis.bil')
#write(Sig_phi_non_dis, outFileNonDis, 1, 6)
#write_xml(outFileNonDis, length, width)
return None #Sig_phi_iono, Sig_phi_nonDisp
def lowPassFilter(dataFile, sigDataFile, maskFile, Sx, Sy, sig_x, sig_y, iteration=5, theta=0.0):
def lowPassFilter(self,dataFile, sigDataFile, maskFile, Sx, Sy, sig_x, sig_y, iteration=5, theta=0.0):
ds = gdal.Open(dataFile + '.vrt', gdal.GA_ReadOnly)
length = ds.RasterYSize
width = ds.RasterXSize
@ -159,7 +123,7 @@ def lowPassFilter(dataFile, sigDataFile, maskFile, Sx, Sy, sig_x, sig_y, iterati
sigData = np.memmap(sigDataFile, dtype=np.float32, mode='r', shape=(length,width))
mask = np.memmap(maskFile, dtype=np.byte, mode='r', shape=(length,width))
dataF, sig_dataF = iterativeFilter(dataIn[:,:], mask[:,:], sigData[:,:], iteration, Sx, Sy, sig_x, sig_y, theta)
dataF, sig_dataF = iterativeFilter(self,dataIn[:,:], mask[:,:], sigData[:,:], iteration, Sx, Sy, sig_x, sig_y, theta)
filtDataFile = dataFile + ".filt"
sigFiltDataFile = sigDataFile + ".filt"
@ -192,7 +156,7 @@ def write_xml(fileName,width,length,bands,dataType,scheme):
return None
def iterativeFilter(dataIn, mask, Sig_dataIn, iteration, Sx, Sy, sig_x, sig_y, theta=0.0):
def iterativeFilter(self,dataIn, mask, Sig_dataIn, iteration, Sx, Sy, sig_x, sig_y, theta=0.0):
data = np.zeros(dataIn.shape)
data[:,:] = dataIn[:,:]
Sig_data = np.zeros(dataIn.shape)
@ -201,17 +165,30 @@ def iterativeFilter(dataIn, mask, Sig_dataIn, iteration, Sx, Sy, sig_x, sig_y, t
print ('masking the data')
data[mask==0]=np.nan
Sig_data[mask==0]=np.nan
print ('Filling the holes with nearest neighbor interpolation')
dataF = fill(data)
Sig_data = fill(Sig_data)
if self.dispersive_filling_method == "smoothed":
print('Filling the holes with smoothed values')
dataF = fill_with_smoothed(data,3)
Sig_data = fill_with_smoothed(Sig_data,3)
else:
print ('Filling the holes with nearest neighbor interpolation')
dataF = fill(data)
Sig_data = fill(Sig_data)
print ('Low pass Gaussian filtering the interpolated data')
dataF, Sig_dataF = Filter(dataF, Sig_data, Sx, Sy, sig_x, sig_y, theta=0.0)
for i in range(iteration):
print ('iteration: ', i , ' of ',iteration)
print ('masking the interpolated and filtered data')
dataF[mask==0]=np.nan
print('Filling the holes with nearest neighbor interpolation of the filtered data from previous step')
dataF = fill(dataF)
if self.dispersive_filling_method == "smoothed":
print("Fill the holes with smoothed values")
dataF = fill_with_smoothed(dataF,3)
else:
print('Filling the holes with nearest neighbor interpolation of the filtered data from previous step')
dataF = fill(dataF)
print('Replace the valid pixels with original unfiltered data')
dataF[mask==1]=data[mask==1]
dataF, Sig_dataF = Filter(dataF, Sig_data, Sx, Sy, sig_x, sig_y, theta=0.0)
@ -219,6 +196,9 @@ def iterativeFilter(dataIn, mask, Sig_dataIn, iteration, Sx, Sy, sig_x, sig_y, t
return dataF, Sig_dataF
def Filter(data, Sig_data, Sx, Sy, sig_x, sig_y, theta=0.0):
import cv2
kernel = Gaussian_kernel(Sx, Sy, sig_x, sig_y) #(800, 800, 15.0, 100.0)
kernel = rotate(kernel , theta)
@ -227,11 +207,6 @@ def Filter(data, Sig_data, Sx, Sy, sig_x, sig_y, theta=0.0):
W1 = cv2.filter2D(1.0/Sig_data**2,-1,kernel)
W2 = cv2.filter2D(1.0/Sig_data**2,-1,kernel**2)
#data = ndimage.convolve(data,kernel, mode='nearest')
#W1 = ndimage.convolve(1.0/Sig_data**2,kernel, mode='nearest')
#W2 = ndimage.convolve(1.0/Sig_data**2,kernel**2, mode='nearest')
return data/W1, np.sqrt(W2/(W1**2))
def Gaussian_kernel(Sx, Sy, sig_x,sig_y):
@ -281,7 +256,34 @@ def rotate(k , theta):
k = a*k
return k
def fill_with_smoothed(off,filterSize):
from astropy.convolution import convolve
off_2filt=np.copy(off)
kernel = np.ones((filterSize,filterSize),np.float32)/(filterSize*filterSize)
loop = 0
cnt2=1
while (cnt2!=0 & loop<100):
loop += 1
idx2= np.isnan(off_2filt)
cnt2 = np.sum(np.count_nonzero(np.isnan(off_2filt)))
print(cnt2)
if cnt2 != 0:
off_filt= convolve(off_2filt,kernel,boundary='extend',nan_treatment='interpolate')
off_2filt[idx2]=off_filt[idx2]
idx3 = np.where(off_filt == 0)
off_2filt[idx3]=np.nan
off_filt=None
return off_2filt
def fill(data, invalid=None):
from scipy import ndimage
"""
Replace the value of invalid 'data' cells (indicated by 'invalid')
by the value of the nearest valid data cell
@ -295,8 +297,6 @@ def fill(data, invalid=None):
Output:
Return a filled array.
"""
from scipy import ndimage
if invalid is None: invalid = np.isnan(data)
ind = ndimage.distance_transform_edt(invalid,
@ -305,7 +305,9 @@ def fill(data, invalid=None):
return data[tuple(ind)]
def getMask(self, maskFile):
def getMask(self, maskFile,std_iono):
from scipy.ndimage import median_filter
ifgDirname = os.path.join(self.insar.ifgDirname, self.insar.lowBandSlcDirname)
lowBandIgram = os.path.join(ifgDirname , 'filt_' + self.insar.ifgFilename )
@ -329,7 +331,7 @@ def getMask(self, maskFile):
else:
highBandIgram += '.unw'
if self.dispersive_filter_mask_type == "coherence":
if (self.dispersive_filter_mask_type == "coherence") and (not self.dispersive_filter_mask_type == "median_filter"):
print ('generating a mask based on coherence files of sub-band interferograms with a threshold of {0}'.format(self.dispersive_filter_coherence_threshold))
cmd = 'imageMath.py -e="(a>{0})*(b>{0})" --a={1} --b={2} -t byte -s BIL -o {3}'.format(self.dispersive_filter_coherence_threshold, lowBandCor, highBandCor, maskFile)
os.system(cmd)
@ -339,28 +341,31 @@ def getMask(self, maskFile):
print ('generating a mask based on .conncomp files')
cmd = 'imageMath.py -e="(a>0)*(b>0)" --a={0} --b={1} -t byte -s BIL -o {2}'.format(lowBandIgram + '.conncomp', highBandIgram + '.conncomp', maskFile)
os.system(cmd)
#m = read(lowBandIgram + '.conncomp')
#m = m[0,:,:]
#m = thresholdConnectedComponents(m,minPixelConnComp)
#mask = np.ones_like((m))
#mask[m==0] = 0.0
#m = read(highBandIgram + '.conncomp')
#m = m[0,:,:]
#m = thresholdConnectedComponents(m,minPixelConnComp)
#mask[m==0] = 0.0
elif self.dispersive_filter_mask_type == "median_filter":
print('Generating mask based on median filtering of the raw dispersive component')
#outName = os.path.join(inps.outDir, 'mask0.bil')
#length, width = mask.shape
#write(mask, outName, 1, 6)
#write_xml(outName, length, width)
# Open raw dispersive component (non-filtered, no unwrapping-error corrected)
dispFilename = os.path.join(self.insar.ionosphereDirname,self.insar.dispersiveFilename)
sigFilename = os.path.join(self.insar.ionosphereDirname,self.insar.dispersiveFilename+'.sig')
ds = gdal.Open(dispFilename+'.vrt',gdal.GA_ReadOnly)
disp = ds.GetRasterBand(1).ReadAsArray()
ds=None
mask = (np.abs(disp-median_filter(disp,15))<3*std_iono)
mask = mask.astype(np.float32)
mask.tofile(maskFile)
dims=np.shape(mask)
write_xml(maskFile,dims[1],dims[0],1,"FLOAT","BIL")
else:
print ('generating a mask based on unwrapped files. Pixels with phase = 0 are masked out.')
cmd = 'imageMath.py -e="(a_1!=0)*(b_1!=0)" --a={0} --b={1} -t byte -s BIL -o {2}'.format(lowBandIgram , highBandIgram , maskFile)
os.system(cmd)
def unwrapp_error_correction(f0, B, dispFile, nonDispFile,lowBandIgram, highBandIgram, jumpsFile, y_ref=None, x_ref=None):
def unwrapp_error_correction(f0, B, dispFile, nonDispFile,lowBandIgram, highBandIgram, y_ref=None, x_ref=None):
dFile = os.path.join(os.path.dirname(dispFile) , "dJumps.bil")
mFile = os.path.join(os.path.dirname(dispFile) , "mJumps.bil")
@ -373,27 +378,13 @@ def unwrapp_error_correction(f0, B, dispFile, nonDispFile,lowBandIgram, highBand
refL = 0.0
refH = 0.0
#cmd = 'imageMath.py -e="round(((a_1-{7}) - (b_1-{8}) - (2.0*{0}/3.0/{1})*c + (2.0*{0}/3.0/{1})*f )/2.0/PI)" --a={2} --b={3} --c={4} --f={5} -o {6} -t float32 -s BIL'.format(B, f0, highBandIgram, lowBandIgram, nonDispFile, dispFile, dFile, refH, refL)
cmd = 'imageMath.py -e="round(((a_1+(2.0*PI*g)) - (b_1) - (2.0*{0}/3.0/{1})*c + (2.0*{0}/3.0/{1})*f )/2.0/PI)" --a={2} --b={3} --c={4} --f={5} --g={6} -o {7} -t float32 -s BIL'.format(B, f0, highBandIgram, lowBandIgram, nonDispFile, dispFile, jumpsFile, dFile)
cmd = 'imageMath.py -e="round(((a_1+(2.0*PI)) - (b_1) - (2.0*{0}/3.0/{1})*c + (2.0*{0}/3.0/{1})*f )/2.0/PI)" --a={2} --b={3} --c={4} --f={5} -o {6} -t float32 -s BIL'.format(B, f0, highBandIgram, lowBandIgram, nonDispFile, dispFile, dFile)
print(cmd)
os.system(cmd)
#d = (phH - phL - (2.*B/3./f0)*ph_nondis + (2.*B/3./f0)*ph_iono )/2./PI
#d = np.round(d)
#cmd = 'imageMath.py -e="round(((a_1 - {6}) + (b_1-{7}) - 2.0*c - 2.0*f )/4.0/PI - g/2)" --a={0} --b={1} --c={2} --f={3} --g={4} -o {5} -t float32 -s BIL'.format(lowBandIgram, highBandIgram, nonDispFile, dispFile, dFile, mFile, refL, refH)
cmd = 'imageMath.py -e="round(((a_1 ) + (b_1+(2.0*PI*k)) - 2.0*c - 2.0*f )/4.0/PI - g/2)" --a={0} --b={1} --c={2} --f={3} --g={4} --k={5} -o {6} -t float32 -s BIL'.format(lowBandIgram, highBandIgram, nonDispFile, dispFile, dFile, jumpsFile, mFile)
print(cmd)
os.system(cmd)
#m = (phL + phH - 2*ph_nondis - 2*ph_iono)/4./PI - d/2.
#m = np.round(m)
cmd = 'imageMath.py -e="round(((a_1 ) + (b_1+(2.0*PI)) - 2.0*c - 2.0*f )/4.0/PI - g/2)" --a={0} --b={1} --c={2} --f={3} --g={4} -o {5} -t float32 -s BIL'.format(lowBandIgram, highBandIgram, nonDispFile, dispFile, dFile, mFile)
print(cmd)
os.system(cmd)
return mFile , dFile
@ -451,33 +442,33 @@ def runDispersive(self):
pulseLength = masterFrame.instrument.pulseLength
chirpSlope = masterFrame.instrument.chirpSlope
# Total Bandwidth
B = np.abs(chirpSlope)*pulseLength
###Determine looks
azLooks, rgLooks = self.insar.numberOfLooks( masterFrame, self.posting,
self.numberAzimuthLooks, self.numberRangeLooks)
#########################################################
# make sure the low-band and high-band interferograms have consistent unwrapping errors.
# For this we estimate jumps as the difference of lowBand and highBand phases divided by 2PI
# The assumprion is that bothe interferograms are flattened and the phase difference between them
# is less than 2PI. This assumprion is valid for current sensors. It needs to be evaluated for
# future sensors like NISAR.
jumpsFile = check_consistency(lowBandIgram, highBandIgram, outputDir)
#########################################################
# estimating the dispersive and non-dispersive components
dispersive_nonDispersive(lowBandIgram, highBandIgram, f0, fL, fH, outDispersive, outNonDispersive, jumpsFile)
dispersive_nonDispersive(lowBandIgram, highBandIgram, f0, fL, fH, outDispersive, outNonDispersive)
# If median filter is selected, compute the ionosphere phase standard deviation at a mean coherence value defined by the user
if self.dispersive_filter_mask_type == "median_filter":
coh_thres = self.dispersive_filter_coherence_threshold
std_iono = std_iono_mean_coh(f0,fL,fH,coh_thres,rgLooks,azLooks)
else:
std_iono = None
# generating a mask which will help filtering the estimated dispersive and non-dispersive phase
getMask(self, maskFile)
getMask(self, maskFile,std_iono)
# Calculating the theoretical standard deviation of the estimation based on the coherence of the interferograms
theoretical_variance_fromSubBands(self, f0, fL, fH, B, sigmaDispersive, sigmaNonDispersive, azLooks * rgLooks)
# low pass filtering the dispersive phase
lowPassFilter(outDispersive, sigmaDispersive, maskFile,
lowPassFilter(self,outDispersive, sigmaDispersive, maskFile,
self.kernel_x_size, self.kernel_y_size,
self.kernel_sigma_x, self.kernel_sigma_y,
iteration = self.dispersive_filter_iterations,
@ -485,7 +476,7 @@ def runDispersive(self):
# low pass filtering the non-dispersive phase
lowPassFilter(outNonDispersive, sigmaNonDispersive, maskFile,
lowPassFilter(self,outNonDispersive, sigmaNonDispersive, maskFile,
self.kernel_x_size, self.kernel_y_size,
self.kernel_sigma_x, self.kernel_sigma_y,
iteration = self.dispersive_filter_iterations,
@ -494,21 +485,21 @@ def runDispersive(self):
# Estimating phase unwrapping errors
mFile , dFile = unwrapp_error_correction(f0, B, outDispersive+".filt", outNonDispersive+".filt",
lowBandIgram, highBandIgram, jumpsFile)
lowBandIgram, highBandIgram)
# re-estimate the dispersive and non-dispersive phase components by taking into account the unwrapping errors
outDispersive = outDispersive + ".unwCor"
outNonDispersive = outNonDispersive + ".unwCor"
dispersive_nonDispersive(lowBandIgram, highBandIgram, f0, fL, fH, outDispersive, outNonDispersive, jumpsFile, m=mFile , d=dFile)
dispersive_nonDispersive(lowBandIgram, highBandIgram, f0, fL, fH, outDispersive, outNonDispersive, m=mFile , d=dFile)
# low pass filtering the new estimations
lowPassFilter(outDispersive, sigmaDispersive, maskFile,
lowPassFilter(self,outDispersive, sigmaDispersive, maskFile,
self.kernel_x_size, self.kernel_y_size,
self.kernel_sigma_x, self.kernel_sigma_y,
iteration = self.dispersive_filter_iterations,
theta = self.kernel_rotation)
lowPassFilter(outNonDispersive, sigmaNonDispersive, maskFile,
lowPassFilter(self,outNonDispersive, sigmaNonDispersive, maskFile,
self.kernel_x_size, self.kernel_y_size,
self.kernel_sigma_x, self.kernel_sigma_y,
iteration = self.dispersive_filter_iterations,