def enl(image, scale=10):
# construct the determinant image
detmap = {
'k': image.select(0),
'ar': image.select(1),
'ai': image.select(2),
'pr': image.select(3),
'pi': image.select(4),
's': image.select(5),
'br': image.select(6),
'bi': image.select(7),
'z': image.select(8)
}
expr = 'k*s*z+2*(ar*br*pr-ai*bi*pr+ai*br*pi+ar*bi*pi)-s*(pr*pr+pi*pi)-k*(br*br+bi*bi)-s*(ar*ar+ai*ai)'
bands = image.bandNames().length()
result = ee.Image(ee.Algorithms.If(bands.lte(3), image.select(0), None))
result = ee.Image(
ee.Algorithms.If(bands.eq(4),
image.expression('b(0)*b(3)-b(1)*b(1)-b(2)*b(2)'),
result))
detimg = ee.Image(
ee.Algorithms.If(bands.eq(9), image.expression(expr, detmap), result))
# 7x7 window average of log of determinant image
avlogdetimg = detimg.log().reduceNeighborhood(ee.Reducer.mean(),
ee.Kernel.square(3.5),
optimization='window')
# log of 7x7 wíndow average of the determinant image
logavdetimg = detimg.reduceNeighborhood(ee.Reducer.mean(),
ee.Kernel.square(3.5),
optimization='window').log()
# add the lookuptable as 500 bands
bands = bands.getInfo()
if bands == 9:
d = 2
elif bands == 4:
d = 1
else:
d = 0
lu = lookup.table()
diff = avlogdetimg.subtract(logavdetimg)
arrimg = diff.add(ee.Image.constant(list(lu[:500, d])))
# shift and multiply to locate zero crossings
arrimg1 = diff.add(ee.Image.constant(list(np.roll(lu[:500, d], 1))))
arrimg = arrimg.multiply(arrimg1)
# accumulate enl histogram
first = ee.Dictionary({
'arrimg': arrimg,
'scale': scale,
'enlhist': ee.List(list(np.zeros(500)))
})
lst = ee.List(list(range(500)))
result = ee.List(
ee.Dictionary(lst.iterate(enl_iter, first)).get('enlhist'))
idx = 10 + d * 10
return result.set(idx, 0).set(0, 0)
def enl(infile,dims=None,outfile='enl.tif',fileout=False,xrange=50,sfn=None):
try:
gdal.AllRegister()
inDataset = gdal.Open(infile,GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
if dims == None:
dims = [0,0,cols,rows]
x0,y0,cols,rows = dims
print( '=========================' )
print( ' ENL Estimation' )
print( '=========================' )
print( time.asctime() )
print( 'infile: %s'%infile )
if bands == 9:
print( 'Quad polarimetry' )
# T11 (k)
band = inDataset.GetRasterBand(1)
k = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# T12 (a)
band = inDataset.GetRasterBand(2)
a = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(3)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
a = a + 1j*im
# T13 (rho)
band = inDataset.GetRasterBand(4)
rho = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(5)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
rho = rho + 1j*im
# T22 (xsi)
band = inDataset.GetRasterBand(6)
xsi = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# T23 (b)
band = inDataset.GetRasterBand(7)
b = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(8)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
b = b + 1j*im
# T33 (zeta)
band = inDataset.GetRasterBand(9)
zeta = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
det = k*xsi*zeta + 2*np.real(a*b*np.conj(rho)) - xsi*(abs(rho)**2) - k*(abs(b)**2) - zeta*(abs(a)**2)
d = 2
elif bands == 4:
print( 'Dual polarimetry' )
# C11 (k)
band = inDataset.GetRasterBand(1)
k = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# C12 (a)
band = inDataset.GetRasterBand(2)
a = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(3)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
a = a + 1j*im
# C22 (xsi)
band = inDataset.GetRasterBand(4)
xsi = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
det = k*xsi - abs(a)**2
d = 1
elif bands <= 3:
print( 'Diagonal-only polarimetry, using first band' )
# C11 (k)
band = inDataset.GetRasterBand(1)
k = band.ReadAsArray(x0,y0,cols,rows).ravel()
det = k
d = 0
enl_ml = np.zeros((rows,cols), dtype= np.float32)
lu = lookup.table()
print( 'filtering...' )
print( 'row: ',end='' )
start = time.time()
for i in range(3,rows-3):
if i%100 == 0:
print( '%i '%i,end='' )
windex = get_windex(i,cols)
for j in range(3,cols-3):
detC = det[windex]
if np.min(detC) > 0.0:
avlogdetC = np.sum(np.log(detC))/49
if bands == 9:
k1 = np.sum(k[windex])/49
a1 = np.sum(a[windex])/49
rho1 = np.sum(rho[windex])/49
xsi1 = np.sum(xsi[windex])/49
b1 = np.sum(b[windex])/49
zeta1 = np.sum(zeta[windex])/49
detavC = k1*xsi1*zeta1 + 2*np.real(a1*b1*np.conj(rho1)) - xsi1*(np.abs(rho1)**2) - k1*(np.abs(b1)**2) - zeta1*(np.abs(a1)**2)
elif bands == 4:
k1 = np.sum(k[windex])/49
xsi1 = np.sum(xsi[windex])/49
a1 = np.sum(a[windex])/49
detavC = k1*xsi1 - np.abs(a1)**2
else:
detavC = np.sum(k[windex])/49
logdetavC = np.log(detavC)
arr = avlogdetC - logdetavC + lu[:,d]
ell = np.where(arr*np.roll(arr,1)<0)[0]
if len(ell)>0:
enl_ml[i,j] = float(ell[-1])/10.0
windex += 1
if fileout:
driver = inDataset.GetDriver()
outDataset = driver.Create(outfile,cols,rows,1,GDT_Float32)
projection = inDataset.GetProjection()
geotransform = inDataset.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x0*gt[1]
gt[3] = gt[3] + y0*gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
outBand = outDataset.GetRasterBand(1)
outBand.WriteArray(enl_ml,0,0)
outBand.FlushCache()
outDataset = None
print( '\nENL image written to: %s'%outfile )
ya,xa = np.histogram(enl_ml,bins=1000)
ya[0:20] = 0.0
print( '\nMode: %f'%xa[np.argmax(ya)] )
plt.plot(xa[1:-1],ya[1:])
plt.title('Histogram ENL for %s'%infile)
plt.xlim([0,xrange])
plt.grid()
if sfn is not None:
plt.savefig(sfn,bbox_inches='tight')
print('\nPlot saved to %s\n'%sfn)
plt.show()
print( 'elapsed time: '+str(time.time()-start) )
return True
except Exception as e:
print( 'error in enlml: %s'%e )
def main():
gdal.AllRegister()
path = auxil.select_directory('Choose working directory')
if path:
os.chdir(path)
# SAR image
infile = auxil.select_infile(title='Choose SAR image')
if infile:
inDataset = gdal.Open(infile, GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
else:
return
# spatial subset
x0, y0, rows, cols = auxil.select_dims([0, 0, rows, cols])
# output file
outfile, fmt = auxil.select_outfilefmt()
if not outfile:
return
print '========================='
print ' ENL Estimation'
print '========================='
print time.asctime()
print 'infile: %s' % infile
start = time.time()
if bands == 9:
print 'Quad polarimetry'
# C11 (k)
band = inDataset.GetRasterBand(1)
k = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
# C12 (a)
band = inDataset.GetRasterBand(2)
a = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
band = inDataset.GetRasterBand(3)
im = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
a = a + 1j * im
# C13 (rho)
band = inDataset.GetRasterBand(4)
rho = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
band = inDataset.GetRasterBand(5)
im = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
rho = rho + 1j * im
# C22 (xsi)
band = inDataset.GetRasterBand(6)
xsi = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
# C23 (b)
band = inDataset.GetRasterBand(7)
b = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
band = inDataset.GetRasterBand(8)
im = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
b = b + 1j * im
# C33 (zeta)
band = inDataset.GetRasterBand(9)
zeta = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
det = k * xsi * zeta + 2 * np.real(a * b * np.conj(rho)) - xsi * (
abs(rho)**2) - k * (abs(b)**2) - zeta * (abs(a)**2)
d = 2
elif bands == 4:
print 'Dual polarimetry'
# C11 (k)
band = inDataset.GetRasterBand(1)
k = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
# C12 (a)
band = inDataset.GetRasterBand(2)
a = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
band = inDataset.GetRasterBand(3)
im = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
a = a + 1j * im
# C22 (xsi)
band = inDataset.GetRasterBand(4)
xsi = np.nan_to_num(band.ReadAsArray(x0, y0, cols, rows)).ravel()
det = k * xsi - abs(a)**2
d = 1
elif bands == 1:
print 'Single polarimetry'
# C11 (k)
band = inDataset.GetRasterBand(1)
k = band.ReadAsArray(x0, y0, cols, rows).ravel()
det = k
d = 0
enl_ml = np.zeros((rows, cols), dtype=np.float32)
lu = lookup.table()
print 'filtering...'
print 'row: ',
sys.stdout.flush()
start = time.time()
for i in range(3, rows - 3):
if i % 50 == 0:
print '%i ' % i,
sys.stdout.flush()
windex = get_windex(i, cols)
for j in range(3, cols - 3):
detC = det[windex]
if np.min(detC) > 0.0:
avlogdetC = np.sum(np.log(detC)) / 49
if bands == 9:
k1 = np.sum(k[windex]) / 49
a1 = np.sum(a[windex]) / 49
rho1 = np.sum(rho[windex]) / 49
xsi1 = np.sum(xsi[windex]) / 49
b1 = np.sum(b[windex]) / 49
zeta1 = np.sum(zeta[windex]) / 49
detavC = k1 * xsi1 * zeta1 + 2 * np.real(
a1 * b1 * np.conj(rho1)) - xsi1 * (
np.abs(rho1)**
2) - k1 * (np.abs(b1)**2) - zeta1 * (np.abs(a1)**2)
elif bands == 4:
k1 = np.sum(k[windex]) / 49
xsi1 = np.sum(xsi[windex]) / 49
a1 = np.sum(a[windex]) / 49
detavC = k1 * xsi1 - np.abs(a1)**2
else:
detavC = np.sum(k[windex]) / 49
logdetavC = np.log(detavC)
arr = avlogdetC - logdetavC + lu[:, d]
ell = np.where(arr * np.roll(arr, 1) < 0)[0]
if ell != []:
enl_ml[i, j] = float(ell[-1]) / 10.0
windex += 1
driver = gdal.GetDriverByName(fmt)
outDataset = driver.Create(outfile, cols, rows, 1, GDT_Float32)
projection = inDataset.GetProjection()
geotransform = inDataset.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x0 * gt[1]
gt[3] = gt[3] + y0 * gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
outBand = outDataset.GetRasterBand(1)
outBand.WriteArray(enl_ml, 0, 0)
outBand.FlushCache()
outDataset = None
ya, xa = np.histogram(enl_ml, bins=50)
ya[0] = 0
plt.plot(xa[0:-1], ya)
plt.show()
print ''
print 'ENL image written to: %s' % outfile
print 'elapsed time: ' + str(time.time() - start)
def main():
usage = '''
Usage:
------------------------------------------------
Calculate the equivalent number of looks for
a polarimetric matrix image
python %s [OPTIONS] filename
Options:
-h this help
-n suppress graphics output
-d spatial subset list e.g. -d [0,0,400,400]
An ENL image will be written to the same directory with '_enl' appended.
------------------------------------------------''' %sys.argv[0]
options,args = getopt.getopt(sys.argv[1:],'hnd:')
dims = None
graphics = True
for option, value in options:
if option == '-h':
print( usage )
return
elif option == '-d':
dims = eval(value)
elif option == '-n':
graphics = False
if len(args) != 1:
print( 'Incorrect number of arguments' )
print( usage )
sys.exit(1)
infile = args[0]
path = os.path.dirname(infile)
basename = os.path.basename(infile)
root, ext = os.path.splitext(basename)
outfile = path + '/' + root + '_enl' + ext
gdal.AllRegister()
inDataset = gdal.Open(infile,GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
if dims == None:
dims = [0,0,cols,rows]
x0,y0,cols,rows = dims
print( '=========================' )
print( ' ENL Estimation' )
print( '=========================' )
print( time.asctime() )
print( 'infile: %s'%infile )
if bands == 9:
print( 'Quad polarimetry' )
# T11 (k)
band = inDataset.GetRasterBand(1)
k = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# T12 (a)
band = inDataset.GetRasterBand(2)
a = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(3)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
a = a + 1j*im
# T13 (rho)
band = inDataset.GetRasterBand(4)
rho = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(5)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
rho = rho + 1j*im
# T22 (xsi)
band = inDataset.GetRasterBand(6)
xsi = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# T23 (b)
band = inDataset.GetRasterBand(7)
b = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(8)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
b = b + 1j*im
# T33 (zeta)
band = inDataset.GetRasterBand(9)
zeta = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
det = k*xsi*zeta + 2*np.real(a*b*np.conj(rho)) - xsi*(abs(rho)**2) - k*(abs(b)**2) - zeta*(abs(a)**2)
d = 2
elif bands == 4:
print( 'Dual polarimetry' )
# C11 (k)
band = inDataset.GetRasterBand(1)
k = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# C12 (a)
band = inDataset.GetRasterBand(2)
a = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(3)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
a = a + 1j*im
# C22 (xsi)
band = inDataset.GetRasterBand(4)
xsi = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
det = k*xsi - abs(a)**2
d = 1
elif bands <= 3:
print( 'Diagonal-only polarimetry' )
# C11 (k)
band = inDataset.GetRasterBand(1)
k = band.ReadAsArray(x0,y0,cols,rows).ravel()
det = k
d = 0
enl_ml = np.zeros((rows,cols), dtype= np.float32)
lu = lookup.table()
print( 'filtering...' )
print( 'row: ', )
sys.stdout.flush()
start = time.time()
for i in range(3,rows-3):
if i%100 == 0:
print( '%i '%i, )
sys.stdout.flush()
windex = get_windex(i,cols)
for j in range(3,cols-3):
detC = det[windex]
if np.min(detC) > 0.0:
avlogdetC = np.sum(np.log(detC))/49
if bands == 9:
k1 = np.sum(k[windex])/49
a1 = np.sum(a[windex])/49
rho1 = np.sum(rho[windex])/49
xsi1 = np.sum(xsi[windex])/49
b1 = np.sum(b[windex])/49
zeta1 = np.sum(zeta[windex])/49
detavC = k1*xsi1*zeta1 + 2*np.real(a1*b1*np.conj(rho1)) - xsi1*(np.abs(rho1)**2) - k1*(np.abs(b1)**2) - zeta1*(np.abs(a1)**2)
elif bands == 4:
k1 = np.sum(k[windex])/49
xsi1 = np.sum(xsi[windex])/49
a1 = np.sum(a[windex])/49
detavC = k1*xsi1 - np.abs(a1)**2
else:
detavC = np.sum(k[windex])/49
logdetavC = np.log(detavC)
arr = avlogdetC - logdetavC + lu[:,d]
ell = np.where(arr*np.roll(arr,1)<0)[0]
if len(ell) >0:
enl_ml[i,j] = float(ell[-1])/10.0
windex += 1
driver = inDataset.GetDriver()
outDataset = driver.Create(outfile,cols,rows,1,GDT_Float32)
projection = inDataset.GetProjection()
geotransform = inDataset.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x0*gt[1]
gt[3] = gt[3] + y0*gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
outBand = outDataset.GetRasterBand(1)
outBand.WriteArray(enl_ml,0,0)
outBand.FlushCache()
outDataset = None
ya,xa = np.histogram(enl_ml,bins=500)
ya[0] = 0
if graphics:
plt.plot(xa[0:-1],ya)
plt.title('Histogram ENL for %s'%infile)
plt.show()
print( 'ENL image written to: %s'%outfile )
print( 'elapsed time: '+str(time.time()-start) )
def main():
usage = '''
Usage:
------------------------------------------------
Calculate the equivalent number of looks for
a polarimetric matrix image
python %s [OPTIONS] filename
Options:
-h this help
-n suppress graphics output
-d spatial subset list e.g. -d [0,0,400,400]
An ENL image will be written to the same directory with '_enl' appended.
------------------------------------------------''' %sys.argv[0]
options,args = getopt.getopt(sys.argv[1:],'hnd:')
dims = None
graphics = True
for option, value in options:
if option == '-h':
print usage
return
elif option == '-d':
dims = eval(value)
elif option == '-n':
graphics = False
if len(args) != 1:
print 'Incorrect number of arguments'
print usage
sys.exit(1)
infile = args[0]
path = os.path.abspath(infile)
dirn = os.path.dirname(path)
basename = os.path.basename(infile)
root, ext = os.path.splitext(basename)
outfile = dirn + '/' + root + '_enl' + ext
gdal.AllRegister()
inDataset = gdal.Open(infile,GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
if dims == None:
dims = [0,0,cols,rows]
x0,y0,cols,rows = dims
print '========================='
print ' ENL Estimation'
print '========================='
print time.asctime()
print 'infile: %s'%infile
if bands == 9:
print 'Quad polarimetry'
# T11 (k)
band = inDataset.GetRasterBand(1)
k = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# T12 (a)
band = inDataset.GetRasterBand(2)
a = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(3)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
a = a + 1j*im
# T13 (rho)
band = inDataset.GetRasterBand(4)
rho = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(5)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
rho = rho + 1j*im
# T22 (xsi)
band = inDataset.GetRasterBand(6)
xsi = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# T23 (b)
band = inDataset.GetRasterBand(7)
b = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(8)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
b = b + 1j*im
# T33 (zeta)
band = inDataset.GetRasterBand(9)
zeta = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
det = k*xsi*zeta + 2*np.real(a*b*np.conj(rho)) - xsi*(abs(rho)**2) - k*(abs(b)**2) - zeta*(abs(a)**2)
d = 2
elif bands == 4:
print 'Dual polarimetry'
# C11 (k)
band = inDataset.GetRasterBand(1)
k = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# C12 (a)
band = inDataset.GetRasterBand(2)
a = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(3)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
a = a + 1j*im
# C22 (xsi)
band = inDataset.GetRasterBand(4)
xsi = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
det = k*xsi - abs(a)**2
d = 1
elif bands == 1:
print 'Single polarimetry'
# C11 (k)
band = inDataset.GetRasterBand(1)
k = band.ReadAsArray(x0,y0,cols,rows).ravel()
det = k
d = 0
enl_ml = np.zeros((rows,cols), dtype= np.float32)
lu = lookup.table()
print 'filtering...'
print 'row: ',
sys.stdout.flush()
start = time.time()
for i in range(3,rows-3):
if i%100 == 0:
print '%i '%i,
sys.stdout.flush()
windex = get_windex(i,cols)
for j in range(3,cols-3):
detC = det[windex]
if np.min(detC) > 0.0:
avlogdetC = np.sum(np.log(detC))/49
if bands == 9:
k1 = np.sum(k[windex])/49
a1 = np.sum(a[windex])/49
rho1 = np.sum(rho[windex])/49
xsi1 = np.sum(xsi[windex])/49
b1 = np.sum(b[windex])/49
zeta1 = np.sum(zeta[windex])/49
detavC = k1*xsi1*zeta1 + 2*np.real(a1*b1*np.conj(rho1)) - xsi1*(np.abs(rho1)**2) - k1*(np.abs(b1)**2) - zeta1*(np.abs(a1)**2)
elif bands == 4:
k1 = np.sum(k[windex])/49
xsi1 = np.sum(xsi[windex])/49
a1 = np.sum(a[windex])/49
detavC = k1*xsi1 - np.abs(a1)**2
else:
detavC = np.sum(k[windex])/49
logdetavC = np.log(detavC)
arr = avlogdetC - logdetavC + lu[:,d]
ell = np.where(arr*np.roll(arr,1)<0)[0]
if ell != []:
enl_ml[i,j] = float(ell[-1])/10.0
windex += 1
driver = inDataset.GetDriver()
outDataset = driver.Create(outfile,cols,rows,1,GDT_Float32)
projection = inDataset.GetProjection()
geotransform = inDataset.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x0*gt[1]
gt[3] = gt[3] + y0*gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
outBand = outDataset.GetRasterBand(1)
outBand.WriteArray(enl_ml,0,0)
outBand.FlushCache()
outDataset = None
ya,xa = np.histogram(enl_ml,bins=500)
ya[0] = 0
if graphics:
plt.plot(xa[0:-1],ya)
plt.title('Histogram ENL for %s'%infile)
plt.show()
print 'ENL image written to: %s'%outfile
print 'elapsed time: '+str(time.time()-start)
def main():
gdal.AllRegister()
path = auxil.select_directory('Choose working directory')
if path:
os.chdir(path)
# SAR image
infile = auxil.select_infile(title='Choose SAR image')
if infile:
inDataset = gdal.Open(infile,GA_ReadOnly)
cols = inDataset.RasterXSize
rows = inDataset.RasterYSize
bands = inDataset.RasterCount
else:
return
# spatial subset
x0,y0,rows,cols=auxil.select_dims([0,0,rows,cols])
# output file
outfile,fmt = auxil.select_outfilefmt()
if not outfile:
return
print '========================='
print ' ENL Estimation'
print '========================='
print time.asctime()
print 'infile: %s'%infile
start = time.time()
if bands == 9:
print 'Quad polarimetry'
# C11 (k)
band = inDataset.GetRasterBand(1)
k = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# C12 (a)
band = inDataset.GetRasterBand(2)
a = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(3)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
a = a + 1j*im
# C13 (rho)
band = inDataset.GetRasterBand(4)
rho = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(5)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
rho = rho + 1j*im
# C22 (xsi)
band = inDataset.GetRasterBand(6)
xsi = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# C23 (b)
band = inDataset.GetRasterBand(7)
b = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(8)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
b = b + 1j*im
# C33 (zeta)
band = inDataset.GetRasterBand(9)
zeta = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
det = k*xsi*zeta + 2*np.real(a*b*np.conj(rho)) - xsi*(abs(rho)**2) - k*(abs(b)**2) - zeta*(abs(a)**2)
d = 2
elif bands == 4:
print 'Dual polarimetry'
# C11 (k)
band = inDataset.GetRasterBand(1)
k = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
# C12 (a)
band = inDataset.GetRasterBand(2)
a = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
band = inDataset.GetRasterBand(3)
im = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
a = a + 1j*im
# C22 (xsi)
band = inDataset.GetRasterBand(4)
xsi = np.nan_to_num(band.ReadAsArray(x0,y0,cols,rows)).ravel()
det = k*xsi - abs(a)**2
d = 1
elif bands == 1:
print 'Single polarimetry'
# C11 (k)
band = inDataset.GetRasterBand(1)
k = band.ReadAsArray(x0,y0,cols,rows).ravel()
det = k
d = 0
enl_ml = np.zeros((rows,cols), dtype= np.float32)
lu = lookup.table()
print 'filtering...'
print 'row: ',
sys.stdout.flush()
start = time.time()
for i in range(3,rows-3):
if i%50 == 0:
print '%i '%i,
sys.stdout.flush()
windex = get_windex(i,cols)
for j in range(3,cols-3):
detC = det[windex]
if np.min(detC) > 0.0:
avlogdetC = np.sum(np.log(detC))/49
if bands == 9:
k1 = np.sum(k[windex])/49
a1 = np.sum(a[windex])/49
rho1 = np.sum(rho[windex])/49
xsi1 = np.sum(xsi[windex])/49
b1 = np.sum(b[windex])/49
zeta1 = np.sum(zeta[windex])/49
detavC = k1*xsi1*zeta1 + 2*np.real(a1*b1*np.conj(rho1)) - xsi1*(np.abs(rho1)**2) - k1*(np.abs(b1)**2) - zeta1*(np.abs(a1)**2)
elif bands == 4:
k1 = np.sum(k[windex])/49
xsi1 = np.sum(xsi[windex])/49
a1 = np.sum(a[windex])/49
detavC = k1*xsi1 - np.abs(a1)**2
else:
detavC = np.sum(k[windex])/49
logdetavC = np.log(detavC)
arr = avlogdetC - logdetavC + lu[:,d]
ell = np.where(arr*np.roll(arr,1)<0)[0]
if ell != []:
enl_ml[i,j] = float(ell[-1])/10.0
windex += 1
driver = gdal.GetDriverByName(fmt)
outDataset = driver.Create(outfile,cols,rows,1,GDT_Float32)
projection = inDataset.GetProjection()
geotransform = inDataset.GetGeoTransform()
if geotransform is not None:
gt = list(geotransform)
gt[0] = gt[0] + x0*gt[1]
gt[3] = gt[3] + y0*gt[5]
outDataset.SetGeoTransform(tuple(gt))
if projection is not None:
outDataset.SetProjection(projection)
outBand = outDataset.GetRasterBand(1)
outBand.WriteArray(enl_ml,0,0)
outBand.FlushCache()
outDataset = None
ya,xa = np.histogram(enl_ml,bins=50)
ya[0] = 0
plt.plot(xa[0:-1],ya)
plt.show()
print ''
print 'ENL image written to: %s'%outfile
print 'elapsed time: '+str(time.time()-start)
