def prune_network(date12_list, inps):
"""Pruning network candidates based on temp/perp baseline and DOP overlap"""
# Turn off pruning for some methods
if inps.referenceFile or inps.method in ['star', 'hierarchical', 'mst']:
inps.threshold = False
if inps.threshold:
# Temporal baseline
date12_list = pnet.threshold_temporal_baseline(date12_list,
inps.tempBaseMax,
inps.keepSeasonal,
inps.tempBaseMin)
log('number of interferograms with temporal baseline in <{}, {}> days: {}'
.format(inps.tempBaseMin, inps.tempBaseMax, len(date12_list)))
if inps.keepSeasonal:
log('\tkeep seasonal pairs, i.e. pairs with temporal baseline == N*years +/- one month'
)
# Perpendicular spatial baseline
date12_list = pnet.threshold_perp_baseline(date12_list, inps.date_list,
inps.pbase_list,
inps.perpBaseMax)
log('number of interferograms with perp baseline <= {} meters: {}'.
format(inps.perpBaseMax, len(date12_list)))
# Doppler Overlap Percentage
if inps.sensor and inps.dop_list:
bandwidth_az = sensor.azimuth_bandwidth(inps.sensor)
date12_list = pnet.threshold_doppler_overlap(
date12_list, inps.date_list, inps.dop_list, bandwidth_az,
inps.dopOverlapMin / 100.0)
log('number of interferograms with azimuth Doppler frequency overlap >= {}%: {}'
.format(inps.dopOverlapMin, len(date12_list)))
if not date12_list:
raise Exception('No interferogram left after pruning!')
# print stat info
m_dates = [
date12.replace('_', '-').split('-')[0] for date12 in date12_list
]
s_dates = [
date12.replace('_', '-').split('-')[1] for date12 in date12_list
]
log('number of acquisitions input : {}'.format(len(inps.date_list)))
log('number of acquisitions selected: {}'.format(
len(list(set(m_dates + s_dates)))))
log('number of interferograms selected: {}'.format(len(date12_list)))
return date12_list
def simulate_coherence(date12_list, baseline_file='bl_list.txt', sensor_name='Env', inc_angle=22.8,
decor_time=200.0, coh_resid=0.2, display=False):
"""Simulate coherence for a given set of interferograms
Inputs:
date12_list - list of string in YYMMDD-YYMMDD format, indicating pairs configuration
baseline_file - string, path of baseline list text file
sensor_name - string, SAR sensor name
inc_angle - float, incidence angle
decor_time - float / 2D np.array in size of (1, pixel_num)
decorrelation rate in days, time for coherence to drop to 1/e of its initial value
coh_resid - float / 2D np.array in size of (1, pixel_num)
long-term coherence, minimum attainable coherence value
display - bool, display result as matrix or not
Output:
cohs - 2D np.array in size of (ifgram_num, pixel_num)
Example:
date12_list = pnet.get_date12_list('ifgram_list.txt')
cohs = simulate_coherences(date12_list, 'bl_list.txt', sensor_name='Tsx')
References:
Zebker, H. A., & Villasenor, J. (1992). Decorrelation in interferometric radar echoes.
IEEE-TGRS, 30(5), 950-959.
Hanssen, R. F. (2001). Radar interferometry: data interpretation and error analysis
(Vol. 2). Dordrecht, Netherlands: Kluwer Academic Pub.
Morishita, Y., & Hanssen, R. F. (2015). Temporal decorrelation in L-, C-, and X-band satellite
radar interferometry for pasture on drained peat soils. IEEE-TGRS, 53(2), 1096-1104.
Parizzi, A., Cong, X., & Eineder, M. (2009). First Results from Multifrequency Interferometry.
A comparison of different decorrelation time constants at L, C, and X Band. ESA Scientific
Publications(SP-677), 1-5.
"""
date_list, pbase_list, dop_list = read_baseline_file(baseline_file)[0:3]
tbase_list = ptime.date_list2tbase(date_list)[0]
# Thermal decorrelation (Zebker and Villasenor, 1992, Eq.4)
SNR = sensor.signal2noise_ratio(sensor_name)
coh_thermal = 1. / (1. + 1./SNR)
pbase_c = critical_perp_baseline(sensor_name, inc_angle)
bandwidth_az = sensor.azimuth_bandwidth(sensor_name)
date12_list = ptime.yyyymmdd_date12(date12_list)
ifgram_num = len(date12_list)
if isinstance(decor_time, (int, float)):
pixel_num = 1
decor_time = float(decor_time)
else:
pixel_num = decor_time.shape[1]
if decor_time == 0.:
decor_time = 0.01
cohs = np.zeros((ifgram_num, pixel_num), np.float32)
for i in range(ifgram_num):
if display:
sys.stdout.write('\rinterferogram = %4d/%4d' % (i, ifgram_num))
sys.stdout.flush()
m_date, s_date = date12_list[i].split('_')
m_idx = date_list.index(m_date)
s_idx = date_list.index(s_date)
pbase = pbase_list[s_idx] - pbase_list[m_idx]
tbase = tbase_list[s_idx] - tbase_list[m_idx]
# Geometric decorrelation (Hanssen, 2001, Eq. 4.4.12)
coh_geom = (pbase_c - abs(pbase)) / pbase_c
if coh_geom < 0.:
coh_geom = 0.
# Doppler centroid decorrelation (Hanssen, 2001, Eq. 4.4.13)
if not dop_list:
coh_dc = 1.
else:
coh_dc = calculate_doppler_overlap(dop_list[m_idx],
dop_list[s_idx],
bandwidth_az)
if coh_dc < 0.:
coh_dc = 0.
# Option 1: Temporal decorrelation - exponential delay model (Parizzi et al., 2009; Morishita and Hanssen, 2015)
coh_temp = np.multiply((coh_thermal - coh_resid), np.exp(-1*abs(tbase)/decor_time)) + coh_resid
coh = coh_geom * coh_dc * coh_temp
cohs[i, :] = coh
#epsilon = 1e-3
#cohs[cohs < epsilon] = epsilon
if display:
print('')
if display:
print(('critical perp baseline: %.f m' % pbase_c))
cohs_mat = coherence_matrix(date12_list, cohs)
plt.figure()
plt.imshow(cohs_mat, vmin=0.0, vmax=1.0, cmap='jet')
plt.xlabel('Image number')
plt.ylabel('Image number')
cbar = plt.colorbar()
cbar.set_label('Coherence')
plt.title('Coherence matrix')
plt.show()
return cohs
def simulate_coherence(date12_list,
baseline_file='bl_list.txt',
sensor_name='Env',
inc_angle=22.8,
decor_time=200.0,
coh_resid=0.2,
display=False):
"""Simulate coherence for a given set of interferograms
Inputs:
date12_list - list of string in YYMMDD-YYMMDD format, indicating pairs configuration
baseline_file - string, path of baseline list text file
sensor_name - string, SAR sensor name
inc_angle - float, incidence angle
decor_time - float / 2D np.array in size of (1, pixel_num)
decorrelation rate in days, time for coherence to drop to 1/e of its initial value
coh_resid - float / 2D np.array in size of (1, pixel_num)
long-term coherence, minimum attainable coherence value
display - bool, display result as matrix or not
Output:
cohs - 2D np.array in size of (ifgram_num, pixel_num)
Example:
date12_list = pnet.get_date12_list('ifgram_list.txt')
cohs = simulate_coherences(date12_list, 'bl_list.txt', sensor_name='Tsx')
References:
Zebker, H. A., & Villasenor, J. (1992). Decorrelation in interferometric radar echoes.
IEEE-TGRS, 30(5), 950-959.
Hanssen, R. F. (2001). Radar interferometry: data interpretation and error analysis
(Vol. 2). Dordrecht, Netherlands: Kluwer Academic Pub.
Morishita, Y., & Hanssen, R. F. (2015). Temporal decorrelation in L-, C-, and X-band satellite
radar interferometry for pasture on drained peat soils. IEEE-TGRS, 53(2), 1096-1104.
Parizzi, A., Cong, X., & Eineder, M. (2009). First Results from Multifrequency Interferometry.
A comparison of different decorrelation time constants at L, C, and X Band. ESA Scientific
Publications(SP-677), 1-5.
"""
date_list, pbase_list, dop_list = read_baseline_file(baseline_file)[0:3]
tbase_list = ptime.date_list2tbase(date_list)[0]
# Thermal decorrelation (Zebker and Villasenor, 1992, Eq.4)
SNR = sensor.signal2noise_ratio(sensor_name)
coh_thermal = 1. / (1. + 1. / SNR)
pbase_c = critical_perp_baseline(sensor_name, inc_angle)
bandwidth_az = sensor.azimuth_bandwidth(sensor_name)
date12_list = ptime.yyyymmdd_date12(date12_list)
ifgram_num = len(date12_list)
if isinstance(decor_time, (int, float)):
pixel_num = 1
decor_time = float(decor_time)
else:
pixel_num = decor_time.shape[1]
if decor_time == 0.:
decor_time = 0.01
cohs = np.zeros((ifgram_num, pixel_num), np.float32)
for i in range(ifgram_num):
if display:
sys.stdout.write('\rinterferogram = %4d/%4d' % (i, ifgram_num))
sys.stdout.flush()
m_date, s_date = date12_list[i].split('_')
m_idx = date_list.index(m_date)
s_idx = date_list.index(s_date)
pbase = pbase_list[s_idx] - pbase_list[m_idx]
tbase = tbase_list[s_idx] - tbase_list[m_idx]
# Geometric decorrelation (Hanssen, 2001, Eq. 4.4.12)
coh_geom = (pbase_c - abs(pbase)) / pbase_c
if coh_geom < 0.:
coh_geom = 0.
# Doppler centroid decorrelation (Hanssen, 2001, Eq. 4.4.13)
if not dop_list:
coh_dc = 1.
else:
coh_dc = calculate_doppler_overlap(dop_list[m_idx],
dop_list[s_idx], bandwidth_az)
if coh_dc < 0.:
coh_dc = 0.
# Option 1: Temporal decorrelation - exponential delay model (Parizzi et al., 2009; Morishita and Hanssen, 2015)
coh_temp = np.multiply(
(coh_thermal - coh_resid), np.exp(
-1 * abs(tbase) / decor_time)) + coh_resid
coh = coh_geom * coh_dc * coh_temp
cohs[i, :] = coh
#epsilon = 1e-3
#cohs[cohs < epsilon] = epsilon
if display:
print('')
if display:
print(('critical perp baseline: %.f m' % pbase_c))
cohs_mat = coherence_matrix(date12_list, cohs)
plt.figure()
plt.imshow(cohs_mat, vmin=0.0, vmax=1.0, cmap='jet')
plt.xlabel('Image number')
plt.ylabel('Image number')
cbar = plt.colorbar()
cbar.set_label('Coherence')
plt.title('Coherence matrix')
plt.show()
return cohs
