def get_padded_feature_stack(window_size, correction_switch, degree, pad=-1):
eigen_full=extract_polarimetric.eigen_raster_full(window_size, correction_switch, degree) #9 is the window size
cov_arr=extract_polarimetric.extract_covariance_arr(window_size, correction_switch, degree)
#padding=int(np.floor(window_size/2))
padding=window_size//2 if pad!=0 else 0
Ihh=np.pad(cov_arr[...,0,0], padding, 'constant')
Ihv=np.pad(cov_arr[...,1,1], padding, 'constant')
Ivv=np.pad(cov_arr[...,2,2], padding, 'constant')
arr_lamb1=np.pad(eigen_full[:,:,2], padding, 'constant')
arr_lamb2=np.pad(eigen_full[:,:,1], padding, 'constant')
arr_lamb3=np.pad(eigen_full[:,:,0], padding, 'constant')
co_pol_dif=np.pad(extract_polarimetric.co_pol_diff(cov_arr), padding, 'constant')
arr_det_cov=np.pad(np.absolute(extract_polarimetric.determinant_cov_conj(cov_arr)), padding, 'constant')
R_co = Rco_X = np.pad(np.real(cov_arr[...,0,2]), padding, 'constant')
Ico_X = np.pad(np.absolute(np.imag(cov_arr[...,0,2])), padding, 'constant')
#return np.dstack((np.real(Ihh),np.real(Ihv),np.real(Ivv),np.real(arr_lamb1), np.real(arr_lamb2), np.real(arr_lamb3), np.absolute(co_pol_dif),R_co, Ico_X, np.absolute(arr_det_cov)))
return np.dstack((np.real(Ihh),np.real(Ihv),np.real(Ivv),np.real(arr_lamb1), np.real(arr_lamb2), np.real(arr_lamb3), np.absolute(co_pol_dif), np.absolute(arr_det_cov)))#,R_co, Ico_X
def plot_feature_space_test(window_size):
eigen_arr = extract_polarimetric.eigen_raster_full(window_size)
cov_arr = extract_polarimetric.extract_covariance_arr(window_size, False)
cppr = np.absolute(extract_polarimetric.co_pol_power_ratio_1(cov_arr))
co_pol_diff = np.absolute(extract_polarimetric.co_pol_diff(cov_arr))
lambda_3 = np.absolute(eigen_arr[:, :, 0])
plot_feature_space(co_pol_diff, lambda_3)
def polarimetric_features(window_size):
fig, ax = plt.subplots(nrows=2, ncols=3)
#arr=extract_polarimetric.eigen_raster_full(window_size)
cov_arr = extract_polarimetric.extract_covariance_arr(window_size, False)
plt.subplot(2, 3, 1)
plt.imshow(np.absolute(extract_polarimetric.co_pol_power_ratio_1(cov_arr)),
cmap='gray')
plt.xlabel('Range')
plt.ylabel('Azimuth')
plt.title('Ivv/Ihh (Co-pol power ratio)')
plt.colorbar()
plt.subplot(2, 3, 2)
plt.imshow(np.absolute(extract_polarimetric.determinant_cov(cov_arr)),
cmap='gray')
plt.xlabel('Range')
plt.ylabel('Azimuth')
plt.title('Det(Cov)')
plt.colorbar()
plt.subplot(2, 3, 3)
plt.imshow(np.absolute(extract_polarimetric.co_pol_diff(cov_arr)),
cmap='gray')
plt.xlabel('Range')
plt.ylabel('Azimuth')
plt.title('Co-pol diff (Ihh-Ivv)')
plt.colorbar()
plt.subplot(2, 3, 4)
plt.imshow(np.real(extract_polarimetric.co_pol_cross_product(cov_arr)),
cmap='gray')
plt.xlabel('Range')
plt.ylabel('Azimuth')
plt.title('Real(ShhSvv) co-pol cross_product')
plt.colorbar()
plt.subplot(2, 3, 5)
plt.imshow(np.imag(extract_polarimetric.co_pol_cross_product(cov_arr)),
cmap='gray')
plt.xlabel('Range')
plt.ylabel('Azimuth')
plt.title('Imag(ShhSvv) Co-pol cross_product')
plt.colorbar()
plt.show()
def read_Pol_features(window_size, correction_switch, degree, rescale_switch = False, log_Transform_switch = False):
#os.chdir('../')
eigen_full = extract_polarimetric.eigen_raster_full(window_size, correction_switch, degree) #9 is the window size
cov_arr = extract_polarimetric.extract_covariance_arr(window_size, correction_switch, degree)
arr_lamb1=np.absolute(eigen_full[:,:,2])
arr_lamb2=np.absolute(eigen_full[:,:,1])
arr_lamb3=np.absolute(eigen_full[:,:,0])
co_pol_dif=np.absolute(extract_polarimetric.co_pol_diff(cov_arr))
arr_det_cov=np.absolute(extract_polarimetric.determinant_cov_conj(cov_arr))
Rco_X = np.real(cov_arr[...,0,2])
Ico_X = abs(np.imag(cov_arr[...,0,2]))
I_hh = np.absolute(cov_arr[...,0,0])
I_hv = np.absolute(cov_arr[...,1,1])
I_vv = np.absolute(cov_arr[...,2,2])
if (log_Transform_switch ==True):
arr_lamb1 = np.log10(arr_lamb1)
arr_lamb2 = np.log10(arr_lamb2)
arr_lamb3 = np.log10(arr_lamb3)
co_pol_dif = np.log10(co_pol_dif)
arr_det_cov = np.log10(arr_det_cov)
Rco_X = np.log10(Rco_X)
Ico_X = np.log10(Ico_X)
I_hh = np.log10(I_hh)
I_hv = np.log10(I_hv)
I_vv = np.log10(I_vv)
if(rescale_switch == True):
arr_lamb1=rescale(arr_lamb1)
arr_lamb2=rescale(arr_lamb2)
arr_lamb3=rescale(arr_lamb3)
co_pol_dif = rescale(co_pol_dif)
arr_det_cov = rescale(arr_det_cov)
Rco_X = rescale(Rco_X)
Ico_X = rescale(Ico_X)
I_hh = rescale(I_hh)
I_hv = rescale(I_hv)
I_vv = rescale(I_vv)
#if(log_Transform_switch ==True):
#return 10*np.log10(np.dstack((arr_lamb1, arr_lamb2, arr_lamb3, co_pol_dif, arr_det_cov, Rco_X, Ico_X, I_hh, I_hv, I_vv)))
#else:
return np.dstack((arr_lamb1, arr_lamb2, arr_lamb3, co_pol_dif, arr_det_cov, Rco_X, Ico_X, I_hh, I_hv, I_vv))
def polarimetric_features(window_size, correction_switch):
#fig, ax = plt.subplots(nrows=2, ncols=3)
#arr=extract_polarimetric.eigen_raster_full(window_size)
cov_arr = extract_polarimetric.extract_covariance_arr(
window_size, correction_switch)
#plt.subplot(2,3,1)
plt.imshow(np.absolute(extract_polarimetric.co_pol_power_ratio_1(cov_arr)),
cmap='gray')
plt.xlabel('Range')
plt.ylabel('Azimuth')
plt.title('Ivv/Ihh (Co-pol power ratio)')
plt.colorbar()
plt.savefig('/home/anurag/Documents/MScProject/Meetings_ITC/Results/' +
'Ivv-Ihh_(Co-pol power ratio)' + '.tiff',
dpi=300)
plt.clf()
#plt.subplot(2,3,2)
plt.imshow(incidence_angle_corr.hist_stretch(
np.absolute(extract_polarimetric.determinant_cov(cov_arr)), 6),
cmap='gray')
plt.xlabel('Range')
plt.ylabel('Azimuth')
plt.title('Det(Cov)')
plt.colorbar()
plt.savefig('/home/anurag/Documents/MScProject/Meetings_ITC/Results/' +
'Det(Cov)_stretched' + '.tiff',
dpi=300)
plt.clf()
#plt.subplot(2,3,2)
plt.imshow(np.absolute(extract_polarimetric.determinant_cov(cov_arr)),
cmap='gray')
plt.xlabel('Range')
plt.ylabel('Azimuth')
plt.title('Det(Cov)')
plt.colorbar()
plt.savefig('/home/anurag/Documents/MScProject/Meetings_ITC/Results/' +
'Det(Cov)' + '.tiff',
dpi=300)
plt.clf()
#plt.subplot(2,3,3)
plt.imshow(np.absolute(extract_polarimetric.co_pol_diff(cov_arr)),
cmap='gray')
plt.xlabel('Range')
plt.ylabel('Azimuth')
plt.title('Co-pol diff (Ihh-Ivv)')
plt.colorbar()
plt.savefig('/home/anurag/Documents/MScProject/Meetings_ITC/Results/' +
'Co-pol diff(Ihh-Ivv)' + '.tiff',
dpi=300)
plt.clf()
#plt.subplot(2,3,4)
plt.imshow(np.real(extract_polarimetric.co_pol_cross_product(cov_arr)),
cmap='gray')
plt.xlabel('Range')
plt.ylabel('Azimuth')
plt.title('Real(ShhSvv) co-pol cross_product')
plt.colorbar()
plt.savefig('/home/anurag/Documents/MScProject/Meetings_ITC/Results/' +
'Real(ShhSvv) co-pol cross_product' + '.tiff',
dpi=300)
plt.clf()
#plt.subplot(2,3,5)
plt.imshow(np.imag(extract_polarimetric.co_pol_cross_product(cov_arr)),
cmap='gray')
plt.xlabel('Range')
plt.ylabel('Azimuth')
plt.title('Imag(ShhSvv) Co-pol cross_product')
plt.colorbar()
plt.savefig('/home/anurag/Documents/MScProject/Meetings_ITC/Results/' +
'Imag(ShhSvv) Co-pol cross_product',
dpi=300)
plt.clf()
def main():
plot_all_UAVSAR_data_takes()
sys.exit()
#UAVSAR_directory_name_prep
base_dir = '../North_Sea_UAVSAR/UAV_norway'
file_ext = [
'.ann', '.dat', '.gif', '.hgt', '.inc', '.kmz', '.slope', '_hgt.tif',
'_pauli.tif'
]
#folders=['_mlc','_grd']
dType = 'grd'
Region = 'norway'
Heading = '007'
Counter_num = '09'
Year = '15'
Num_flights_year = '092'
Data_take = '000'
Day = '10'
Month = '06'
Band = 'L'
Steering_angle = '090'
Cross_talk = 'CX'
Processing_version = '01'
Polarization = 'HHHV'
#====================================================
Is_List_Ratio = False
Cropping_switch = True
Reproject_switch = False
Plotting_switch = True
Convolution_switch = False
Inc_correction_switch = False
Window_size_fea = 9
Inc_correction_sin_degree = 3
#====================================================
wd = getdir(region=Region,
heading=Heading,
counter_num=Counter_num,
year=Year,
num_flights_year=Num_flights_year,
data_take=Data_take,
day=Day,
month=Month,
band=Band,
steering_angle=Steering_angle,
cross_talk=Cross_talk,
processing_version=Processing_version)
base_file_name = get_base_file_name(region=Region,
heading=Heading,
counter_num=Counter_num,
year=Year,
num_flights_year=Num_flights_year,
data_take=Data_take,
day=Day,
month=Month,
band=Band,
steering_angle=Steering_angle,
cross_talk=Cross_talk,
processing_version=Processing_version)
os.chdir(wd)
#print(os.getcwd())
#=======+Get Matadata========
meta = metadata_dict(base_file_name)
#===============SLC========================
#directory_SLC='/home/anurag/Documents/MScProject/SAR/OilSpill/North_Sea_UAVSAR/UAV_norway/UA_norway_00709_15092_000_150610_L090_CX_02'
#print(slc_file_name)
#print(os.getcwd())
#print(meta)
#print(os.getcwd())
#os.chdir(directory_SLC)
#slc_VV=get_SLC(meta,base_file_name, polarization='VV', dType='slc')
#slc_HH=get_SLC(meta,base_file_name, polarization='HH', dType='slc')
#=============Defining the subset=====================
if dType == 'mlc':
grd_cropping_list_00709 = [521, 1543, 4049,
5233] #[2000,3500,2500,4000]
grd_cropping_list_18709 = [1000, 2300, 3250, 4450]
elif dType == 'grd':
grd_cropping_list_00709 = [2000, 3500, 2500, 4000]
grd_cropping_list_18709 = [900, 2400, 3150, 4650]
if Heading == '007':
cropping_list_GRD = grd_cropping_list_00709
if (Counter_num == '10'):
cropping_list_GRD = [2500, 4000, 2500, 4000]
elif Heading == '187':
cropping_list_GRD = grd_cropping_list_18709
if (Counter_num == '10'):
cropping_list_GRD = [1400, 2900, 3150, 4650]
elif Heading == '142':
cropping_list_GRD = [2500, 4000, 2500, 4000]
#===========Defining Convolution keranl===========
Convolution_kernal = kernal(Window_size_fea)
#============incidence angle=================
grd_lines = int(meta['grd_pwr.set_rows'])
grd_samples = int(meta['grd_pwr.set_cols'])
inc_ang_arr = get_inc_angle(grd_lines,
grd_samples,
base_file_name,
cropping_list_GRD,
cropping_switch=Cropping_switch,
is_List_Ratio=Is_List_Ratio)
#plt.imshow(inc_ang_arr)
#plt.show()
#=============Extract GRD Component=====================
get_GRD_MLC(dType,
meta,
base_file_name,
Convolution_kernal,
inc_ang_arr,
component=Polarization,
grd_cropping_list=cropping_list_GRD,
is_List_Ratio=False,
cropping_switch=True,
reproject_switch=False,
plotting_switch=True,
convolution_switch=True,
inc_correction_switch=True)
#=============Plot all data takes=======================
#==================Get covariance matrix===============
cov_arr = get_covariance_matrix_grd(
dType,
meta,
base_file_name,
Convolution_kernal,
inc_ang_arr,
inc_correction_sin_degree=Inc_correction_sin_degree,
cropping_list_GRD=cropping_list_GRD,
is_List_Ratio=Is_List_Ratio,
cropping_switch=Cropping_switch,
convolution_switch=Convolution_switch,
inc_correction_switch=Inc_correction_switch)
#=============Convert to T3=========
coh_arr = get_coherecy_matrix_grd(cov_arr)
#########################################
#============Extraction of Pol. Features===========
#########################################
eigen_raster = eigen_raster_full(coh_arr)
arr_lamb1 = eigen_raster[:, :, 2]
arr_lamb2 = eigen_raster[:, :, 1]
arr_lamb3 = eigen_raster[:, :, 0]
#============get_co_pol_diff============
co_pol_diff = extract_polarimetric.co_pol_diff(cov_arr)
#================Plotting lambda 3 and co_pol diff===============
#import sys
#sys.exit()
plt.subplot(221)
plt.imshow(10 * np.log10(np.absolute(arr_lamb1)))
plt.colorbar()
plt.subplot(222)
plt.imshow(10 * np.log10(np.absolute(arr_lamb2)))
plt.colorbar()
plt.subplot(223)
plt.imshow(10 * np.log10(np.absolute(arr_lamb3)))
plt.colorbar()
plt.subplot(224)
plt.imshow(np.log10(np.absolute(co_pol_diff)))
plt.colorbar()
plt.show()
######################################
#=================EPFS===============
######################################
ncomp = 3
tolerance = 0.0001
num_initialization = 1
max_iteration = 200
correction_switch = True
window_size_fea = 9
window_size_smoo = 1 #35
window_size_boun = 3
bin_size = 400
font_size = 15
#-------Plot histogram--------
arr_1 = EPFS.rescale(np.log10(np.absolute(arr_lamb3)), clip_extremes=False)
arr_3 = EPFS.rescale(np.log10(np.absolute(arr_lamb2)), clip_extremes=False)
arr_4 = EPFS.rescale(np.log10(np.absolute(arr_lamb1)), clip_extremes=False)
arr_2 = EPFS.rescale(np.log10(np.absolute(co_pol_diff)),
clip_extremes=False)
plt.hist(arr_1.flatten(),
bins=bin_size,
rwidth=0.5,
histtype='step',
label='$\lambda_{3}$')
plt.hist(arr_3.flatten(),
bins=bin_size,
rwidth=0.5,
histtype='step',
label='$\lambda_{2}$')
plt.hist(arr_4.flatten(),
bins=bin_size,
rwidth=0.5,
histtype='step',
label='$\lambda_{1}$')
plt.hist(arr_2.flatten(),
bins=bin_size,
rwidth=0.5,
histtype='step',
label='PD')
plt.xlabel('Normalized values', fontsize=font_size)
plt.ylabel('Frequency', fontsize=font_size)
plt.legend()
plt.show()
#==========Stack of features===============
feature_stack = np.dstack((arr_1, arr_2, arr_3, arr_4, cov_arr[..., 2, 2]))
#-------Perform EPFS-----------
gmm = EPFS.gmm_fitting_1(feature_stack,
ncomp,
tolerance=tolerance,
num_initial=num_initialization,
max_iteration=max_iteration)
EPFS.plot_result(feature_stack, gmm, ncomp)
#plt.show()
#=========Co-pol_phase_diff===========
'''
def plot_all_UAVSAR_data_takes():
matplotlib.rcParams.update({'font.size': 3})
#files = ['UA_norway_00709_15091_000_150610_L090_CX_01', 'UA_norway_18709_15091_001_150610_L090_CX_01',
#'UA_norway_00710_15091_002_150610_L090_CX_01', 'UA_norway_18710_15091_003_150610_L090_CX_01',
#'UA_norway_00709_15091_004_150610_L090_CX_01', 'UA_norway_18709_15091_005_150610_L090_CX_01',
#'UA_norway_00710_15091_006_150610_L090_CX_01', 'UA_norway_18709_15091_007_150610_L090_CX_01',
#'UA_norway_00709_15091_008_150610_L090_CX_01', 'UA_norway_18709_15091_009_150610_L090_CX_01',
#'UA_norway_00709_15091_010_150610_L090_CX_01', 'UA_norway_18709_15091_011_150610_L090_CX_01',
#'UA_norway_00709_15091_012_150610_L090_CX_01', 'UA_norway_18709_15091_013_150610_L090_CX_01',
#'UA_norway_00709_15091_014_150610_L090_CX_01', 'UA_norway_14203_15091_015_150610_L090_CX_01',
#'UA_norway_00709_15092_000_150610_L090_CX_01', 'UA_norway_18709_15092_001_150610_L090_CX_01',
#'UA_norway_00709_15092_002_150610_L090_CX_01', 'UA_norway_18709_15092_003_150610_L090_CX_01',
#'UA_norway_00709_15092_004_150610_L090_CX_01', 'UA_norway_18709_15092_005_150610_L090_CX_01']
files = [
'UA_norway_00709_15091_000_150610_L090_CX_01',
'UA_norway_00709_15091_004_150610_L090_CX_01',
'UA_norway_00709_15091_008_150610_L090_CX_01',
'UA_norway_00709_15091_010_150610_L090_CX_01',
'UA_norway_00709_15091_012_150610_L090_CX_01',
'UA_norway_00709_15091_014_150610_L090_CX_01',
'UA_norway_00709_15092_000_150610_L090_CX_01',
'UA_norway_00709_15092_002_150610_L090_CX_01',
'UA_norway_00709_15092_004_150610_L090_CX_01'
]
for i, dir_base_file_name in enumerate(files):
#dType = 'grd'
dType = 'mlc'
Region = 'norway'
Heading = dir_base_file_name[10:13]
Counter_num = dir_base_file_name[13:15]
Year = '15'
Num_flights_year = dir_base_file_name[18:21]
Data_take = dir_base_file_name[22:25]
Day = '10'
Month = '06'
Band = 'L'
Steering_angle = '090'
Cross_talk = 'CX'
Processing_version = '01'
Polarization = 'VVVV'
#====================================================
Is_List_Ratio = False
Cropping_switch = True
Reproject_switch = False
Plotting_switch = True
Convolution_switch = True
Inc_correction_switch = True
Window_size_fea = 9
Inc_correction_sin_degree = 2
#====================================================
ncomp = 6
tolerance = 0.0001
num_initialization = 1
max_iteration = 200
window_size_smoo = 35
bin_size = 400
#=====================================================
wd = getdir(region=Region,
heading=Heading,
counter_num=Counter_num,
year=Year,
num_flights_year=Num_flights_year,
data_take=Data_take,
day=Day,
month=Month,
band=Band,
steering_angle=Steering_angle,
cross_talk=Cross_talk,
processing_version=Processing_version)
base_file_name = get_base_file_name(
region=Region,
heading=Heading,
counter_num=Counter_num,
year=Year,
num_flights_year=Num_flights_year,
data_take=Data_take,
day=Day,
month=Month,
band=Band,
steering_angle=Steering_angle,
cross_talk=Cross_talk,
processing_version=Processing_version)
os.chdir(wd)
#print(os.getcwd())
#=======+Get Matadata========
meta = metadata_dict(base_file_name)
#=============Defining the subset=====================
if dType == 'mlc':
grd_cropping_list_00709 = [521, 1543, 4049,
5233] #[2000,3500,2500,4000]
grd_cropping_list_18709 = [1000, 2300, 3250, 4450]
elif dType == 'grd':
grd_cropping_list_00709 = [2000, 3500, 2500, 4000]
grd_cropping_list_18709 = [900, 2400, 3150, 4650]
if Heading == '007':
cropping_list_GRD = grd_cropping_list_00709
if (Counter_num == '10'):
cropping_list_GRD = [2500, 4000, 2500, 4000]
elif Heading == '187':
cropping_list_GRD = grd_cropping_list_18709
if (Counter_num == '10'):
cropping_list_GRD = [1400, 2900, 3150, 4650]
elif Heading == '142':
cropping_list_GRD = [2500, 4000, 2500, 4000]
#===========Defining Convolution keranl===========
Convolution_kernal = kernal(Window_size_fea)
#============incidence angle=================
grd_lines = int(meta['grd_pwr.set_rows'])
grd_samples = int(meta['grd_pwr.set_cols'])
inc_ang_arr = get_inc_angle(grd_lines,
grd_samples,
base_file_name,
cropping_list_GRD,
cropping_switch=Cropping_switch,
is_List_Ratio=Is_List_Ratio)
#inc_ang_arr = 0
#plt.imshow(inc_ang_arr)
#plt.show()
#=============Extract GRD Component and plotting=====================
plt.subplot(4, 6, i + 1)
#grd_data = get_GRD_MLC(dType,meta, base_file_name,Convolution_kernal, inc_ang_arr, component = Polarization,grd_cropping_list=cropping_list_GRD, is_List_Ratio=False, cropping_switch=True, reproject_switch=False, plotting_switch=True, convolution_switch = True, inc_correction_switch=False, save_plot_switch = False)
#time_stamp = meta['Date of Acquisition'][12:17]
#plt.title(time_stamp + ['AM' if int(time_stamp[:2])<12 else ' PM' ][0], fontsize = 4)
#plt.ylabel('Longitude' if((i)%6==0) else '', fontsize = 7)
#plt.xlabel('Latitude' if (i>=18) else '', fontsize = 7)
#plt.savefig('/home/anurag/Documents/MScProject/Meetings_ITC/Results/All_UAVSAR_plot_grd.tiff', dpi=300,bbox_inches='tight')
#plt.show()
cov_arr = get_covariance_matrix_grd(
dType,
meta,
base_file_name,
Convolution_kernal,
inc_ang_arr,
inc_correction_sin_degree=Inc_correction_sin_degree,
cropping_list_GRD=cropping_list_GRD,
is_List_Ratio=Is_List_Ratio,
cropping_switch=Cropping_switch,
convolution_switch=Convolution_switch,
inc_correction_switch=Inc_correction_switch)
#=============Convert to T3=========
coh_arr = get_coherecy_matrix_grd(cov_arr)
#########################################
#============Extraction of Pol. Features===========
#########################################
eigen_raster = eigen_raster_full(coh_arr)
arr_lamb1 = eigen_raster[:, :, 2]
arr_lamb2 = eigen_raster[:, :, 1]
arr_lamb3 = eigen_raster[:, :, 0]
#============get_co_pol_diff============
co_pol_diff = extract_polarimetric.co_pol_diff(cov_arr)
arr_1 = 10 * np.log10(np.absolute(arr_lamb3))
arr_2 = 10 * np.log10(np.absolute(co_pol_diff))
feature_stack = np.dstack(
(arr_1, arr_2)) #, arr_3, arr_4, cov_arr[...,2,2]))
#-------Perform EPFS-----------
gmm = EPFS.gmm_fitting_1(feature_stack,
ncomp,
tolerance=tolerance,
num_initial=num_initialization,
max_iteration=max_iteration)
X = EPFS.prepare_X_all(feature_stack)
shp = feature_stack.shape[:2]
res = gmm.predict(X).reshape(shp[0], shp[1])
im = plt.imshow(res.reshape(shp[0], shp[1]), cmap='RdYlBu')
plt.ylabel('Azimuth' if ((i) % 6 == 0) else '', fontsize=7)
plt.xlabel('Range' if (i >= 18) else '', fontsize=7)
time_stamp = meta['Date of Acquisition'][12:17]
plt.title(time_stamp +
['AM' if int(time_stamp[:2]) < 12 else ' PM'][0],
fontsize=4)
plt.tight_layout()
#plt.savefig('/home/anurag/Documents/MScProject/Meetings_ITC/Results/Segmentation/Multi_temporal/Segmentation_ncomp_{}_inc_corr_degree_{}_tol10E-4.tiff'.format(str(ncomp),str(Inc_correction_sin_degree)), dpi=300,bbox_inches='tight')
plt.show()