def select_pairs_sequential(date_list,
num_connection=2,
date12_format='YYMMDD-YYMMDD'):
"""Select Pairs in a Sequential way:
For each acquisition, find its num_connection nearest acquisitions in the past time.
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
Reference:
Fattahi, H., and F. Amelung (2013), DEM Error Correction in InSAR Time Series, IEEE TGRS, 51(7), 4249-4259.
"""
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
date_idx_list = list(range(len(date6_list)))
# Get pairs index list
date12_idx_list = []
for date_idx in date_idx_list:
for i in range(num_connection):
if date_idx - i - 1 >= 0:
date12_idx_list.append([date_idx - i - 1, date_idx])
date12_idx_list = [sorted(idx) for idx in sorted(date12_idx_list)]
# Convert index into date12
date12_list = [
date6_list[idx[0]] + '-' + date6_list[idx[1]]
for idx in date12_idx_list
]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_star(date_list, m_date=None, pbase_list=[], date12_format='YYMMDD-YYMMDD'):
"""Select Star-like network/interferograms/pairs, it's a single master network, similar to PS approach.
Usage:
m_date : master date, choose it based on the following cretiria:
1) near the center in temporal and spatial baseline
2) prefer winter season than summer season for less temporal decorrelation
Reference:
Ferretti, A., C. Prati, and F. Rocca (2001), Permanent scatterers in SAR interferometry, IEEE TGRS, 39(1), 8-20.
"""
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
# Select master date if not existed
if not m_date:
m_date = select_master_date(date8_list, pbase_list)
print(('auto select master date: '+m_date))
# Check input master date
m_date8 = ptime.yyyymmdd(m_date)
if m_date8 not in date8_list:
print('Input master date is not existed in date list!')
print(('Input master date: '+str(m_date8)))
print(('Input date list: '+str(date8_list)))
m_date8 = None
# Generate star/ps network
m_idx = date8_list.index(m_date8)
date12_idx_list = [sorted([m_idx, s_idx]) for s_idx in range(len(date8_list))
if s_idx is not m_idx]
date12_list = [date6_list[idx[0]]+'-'+date6_list[idx[1]]
for idx in date12_idx_list]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_sequential(date_list, num_connection=2, date12_format='YYMMDD-YYMMDD'):
"""Select Pairs in a Sequential way:
For each acquisition, find its num_connection nearest acquisitions in the past time.
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
Reference:
Fattahi, H., and F. Amelung (2013), DEM Error Correction in InSAR Time Series, IEEE TGRS, 51(7), 4249-4259.
"""
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
date_idx_list = list(range(len(date6_list)))
# Get pairs index list
date12_idx_list = []
for date_idx in date_idx_list:
for i in range(num_connection):
if date_idx-i-1 >= 0:
date12_idx_list.append([date_idx-i-1, date_idx])
date12_idx_list = [sorted(idx) for idx in sorted(date12_idx_list)]
# Convert index into date12
date12_list = [date6_list[idx[0]]+'-'+date6_list[idx[1]]
for idx in date12_idx_list]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_star(date_list, m_date=None, pbase_list=[], date12_format='YYMMDD-YYMMDD'):
"""Select Star-like network/interferograms/pairs, it's a single master network, similar to PS approach.
Usage:
m_date : master date, choose it based on the following cretiria:
1) near the center in temporal and spatial baseline
2) prefer winter season than summer season for less temporal decorrelation
Reference:
Ferretti, A., C. Prati, and F. Rocca (2001), Permanent scatterers in SAR interferometry, IEEE TGRS, 39(1), 8-20.
"""
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
# Select master date if not existed
if not m_date:
m_date = select_master_date(date8_list, pbase_list)
print(('auto select master date: '+m_date))
# Check input master date
m_date8 = ptime.yyyymmdd(m_date)
if m_date8 not in date8_list:
print('Input master date is not existed in date list!')
print(('Input master date: '+str(m_date8)))
print(('Input date list: '+str(date8_list)))
m_date8 = None
# Generate star/ps network
m_idx = date8_list.index(m_date8)
date12_idx_list = [sorted([m_idx, s_idx]) for s_idx in range(len(date8_list))
if s_idx is not m_idx]
date12_list = [date6_list[idx[0]]+'-'+date6_list[idx[1]]
for idx in date12_idx_list]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_delaunay(date_list, pbase_list, norm=True, date12_format='YYMMDD-YYMMDD'):
"""Select Pairs using Delaunay Triangulation based on temporal/perpendicular baselines
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
pbase_list : list of float, perpendicular spatial baseline
norm : normalize temporal baseline to perpendicular baseline
Key points
1. Define a ratio between perpendicular and temporal baseline axis units (Pepe and Lanari, 2006, TGRS).
2. Pairs with too large perpendicular / temporal baseline or Doppler centroid difference should be removed
after this, using a threshold, to avoid strong decorrelations (Zebker and Villasenor, 1992, TGRS).
Reference:
Pepe, A., and R. Lanari (2006), On the extension of the minimum cost flow algorithm for phase unwrapping
of multitemporal differential SAR interferograms, IEEE TGRS, 44(9), 2374-2383.
Zebker, H. A., and J. Villasenor (1992), Decorrelation in interferometric radar echoes, IEEE TGRS, 30(5), 950-959.
"""
# Get temporal baseline in days
date6_list = ptime.yymmdd(date_list)
date8_list = ptime.yyyymmdd(date_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Normalization (Pepe and Lanari, 2006, TGRS)
if norm:
temp2perp_scale = (max(pbase_list)-min(pbase_list)) / (max(tbase_list)-min(tbase_list))
tbase_list = [tbase*temp2perp_scale for tbase in tbase_list]
# Generate Delaunay Triangulation
date12_idx_list = Triangulation(tbase_list, pbase_list).edges.tolist()
date12_idx_list = [sorted(idx) for idx in sorted(date12_idx_list)]
# Convert index into date12
date12_list = [date6_list[idx[0]]+'-'+date6_list[idx[1]]
for idx in date12_idx_list]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def get_date12_list(fname, dropIfgram=False):
"""Read date12 info from input file: Pairs.list or multi-group hdf5 file
Parameters: fname - string, path/name of input multi-group hdf5 file or text file
dropIfgram - bool, check the "dropIfgram" dataset in ifgramStack hdf5 file
Returns: date12_list - list of string in YYYYMMDD_YYYYMMDD format
Example:
date12List = get_date12_list('ifgramStack.h5')
date12List = get_date12_list('ifgramStack.h5', dropIfgram=True)
date12List = get_date12_list('coherenceSpatialAvg.txt')
"""
date12_list = []
ext = os.path.splitext(fname)[1].lower()
if ext == '.h5':
k = readfile.read_attribute(fname)['FILE_TYPE']
if k == 'ifgramStack':
date12_list = ifgramStack(fname).get_date12_list(
dropIfgram=dropIfgram)
else:
return None
else:
date12_list = np.loadtxt(fname, dtype=bytes,
usecols=0).astype(str).tolist()
# for txt file with only one interferogram
if isinstance(date12_list, str):
date12_list = [date12_list]
date12_list = sorted(date12_list)
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_delaunay(date_list, pbase_list, norm=True, date12_format='YYMMDD-YYMMDD'):
"""Select Pairs using Delaunay Triangulation based on temporal/perpendicular baselines
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
pbase_list : list of float, perpendicular spatial baseline
norm : normalize temporal baseline to perpendicular baseline
Key points
1. Define a ratio between perpendicular and temporal baseline axis units (Pepe and Lanari, 2006, TGRS).
2. Pairs with too large perpendicular / temporal baseline or Doppler centroid difference should be removed
after this, using a threshold, to avoid strong decorrelations (Zebker and Villasenor, 1992, TGRS).
Reference:
Pepe, A., and R. Lanari (2006), On the extension of the minimum cost flow algorithm for phase unwrapping
of multitemporal differential SAR interferograms, IEEE TGRS, 44(9), 2374-2383.
Zebker, H. A., and J. Villasenor (1992), Decorrelation in interferometric radar echoes, IEEE TGRS, 30(5), 950-959.
"""
# Get temporal baseline in days
date6_list = ptime.yymmdd(date_list)
date8_list = ptime.yyyymmdd(date_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Normalization (Pepe and Lanari, 2006, TGRS)
if norm:
temp2perp_scale = (max(pbase_list)-min(pbase_list)) / (max(tbase_list)-min(tbase_list))
tbase_list = [tbase*temp2perp_scale for tbase in tbase_list]
# Generate Delaunay Triangulation
date12_idx_list = Triangulation(tbase_list, pbase_list).edges.tolist()
date12_idx_list = [sorted(idx) for idx in sorted(date12_idx_list)]
# Convert index into date12
date12_list = [date6_list[idx[0]]+'-'+date6_list[idx[1]]
for idx in date12_idx_list]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_all(date_list, date12_format='YYMMDD-YYMMDD'):
"""Select All Possible Pairs/Interferograms
Input : date_list - list of date in YYMMDD/YYYYMMDD format
Output: date12_list - list date12 in YYMMDD-YYMMDD format
Reference:
Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti (2002), A new algorithm for surface deformation monitoring
based on small baseline differential SAR interferograms, IEEE TGRS, 40(11), 2375-2383.
"""
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
date12_list = list(itertools.combinations(date6_list, 2))
date12_list = [date12[0] + '-' + date12[1] for date12 in date12_list]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_all(date_list, date12_format='YYMMDD-YYMMDD'):
"""Select All Possible Pairs/Interferograms
Input : date_list - list of date in YYMMDD/YYYYMMDD format
Output: date12_list - list date12 in YYMMDD-YYMMDD format
Reference:
Berardino, P., G. Fornaro, R. Lanari, and E. Sansosti (2002), A new algorithm for surface deformation monitoring
based on small baseline differential SAR interferograms, IEEE TGRS, 40(11), 2375-2383.
"""
date8_list = sorted(ptime.yyyymmdd(date_list))
date6_list = ptime.yymmdd(date8_list)
date12_list = list(itertools.combinations(date6_list, 2))
date12_list = [date12[0]+'-'+date12[1] for date12 in date12_list]
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_mst(date_list, pbase_list, date12_format='YYMMDD-YYMMDD'):
"""Select Pairs using Minimum Spanning Tree technique
Connection Cost is calculated using the baseline distance in perp and scaled temporal baseline (Pepe and Lanari,
2006, TGRS) plane.
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
pbase_list : list of float, perpendicular spatial baseline
References:
Pepe, A., and R. Lanari (2006), On the extension of the minimum cost flow algorithm for phase unwrapping
of multitemporal differential SAR interferograms, IEEE TGRS, 44(9), 2374-2383.
Perissin D., Wang T. (2012), Repeat-pass SAR interferometry with partially coherent targets. IEEE TGRS. 271-280
"""
# Get temporal baseline in days
date6_list = ptime.yymmdd(date_list)
date8_list = ptime.yyyymmdd(date_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Normalization (Pepe and Lanari, 2006, TGRS)
temp2perp_scale = (max(pbase_list) - min(pbase_list)) / (max(tbase_list) -
min(tbase_list))
tbase_list = [tbase * temp2perp_scale for tbase in tbase_list]
# Get weight matrix
ttMat1, ttMat2 = np.meshgrid(np.array(tbase_list), np.array(tbase_list))
ppMat1, ppMat2 = np.meshgrid(np.array(pbase_list), np.array(pbase_list))
ttMat = np.abs(ttMat1 - ttMat2) # temporal distance matrix
ppMat = np.abs(ppMat1 - ppMat2) # spatial distance matrix
# 2D distance matrix in temp/perp domain
weightMat = np.sqrt(np.square(ttMat) + np.square(ppMat))
weightMat = sparse.csr_matrix(weightMat) # compress sparse row matrix
# MST path based on weight matrix
mstMat = sparse.csgraph.minimum_spanning_tree(weightMat)
# Convert MST index matrix into date12 list
[s_idx_list, m_idx_list] = [
date_idx_array.tolist() for date_idx_array in sparse.find(mstMat)[0:2]
]
date12_list = []
for i in range(len(m_idx_list)):
idx = sorted([m_idx_list[i], s_idx_list[i]])
date12 = date6_list[idx[0]] + '-' + date6_list[idx[1]]
date12_list.append(date12)
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_mst(date_list, pbase_list, date12_format='YYMMDD-YYMMDD'):
"""Select Pairs using Minimum Spanning Tree technique
Connection Cost is calculated using the baseline distance in perp and scaled temporal baseline (Pepe and Lanari,
2006, TGRS) plane.
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
pbase_list : list of float, perpendicular spatial baseline
References:
Pepe, A., and R. Lanari (2006), On the extension of the minimum cost flow algorithm for phase unwrapping
of multitemporal differential SAR interferograms, IEEE TGRS, 44(9), 2374-2383.
Perissin D., Wang T. (2012), Repeat-pass SAR interferometry with partially coherent targets. IEEE TGRS. 271-280
"""
# Get temporal baseline in days
date6_list = ptime.yymmdd(date_list)
date8_list = ptime.yyyymmdd(date_list)
tbase_list = ptime.date_list2tbase(date8_list)[0]
# Normalization (Pepe and Lanari, 2006, TGRS)
temp2perp_scale = (max(pbase_list)-min(pbase_list)) / (max(tbase_list)-min(tbase_list))
tbase_list = [tbase*temp2perp_scale for tbase in tbase_list]
# Get weight matrix
ttMat1, ttMat2 = np.meshgrid(np.array(tbase_list), np.array(tbase_list))
ppMat1, ppMat2 = np.meshgrid(np.array(pbase_list), np.array(pbase_list))
ttMat = np.abs(ttMat1 - ttMat2) # temporal distance matrix
ppMat = np.abs(ppMat1 - ppMat2) # spatial distance matrix
# 2D distance matrix in temp/perp domain
weightMat = np.sqrt(np.square(ttMat) + np.square(ppMat))
weightMat = sparse.csr_matrix(weightMat) # compress sparse row matrix
# MST path based on weight matrix
mstMat = sparse.csgraph.minimum_spanning_tree(weightMat)
# Convert MST index matrix into date12 list
[s_idx_list, m_idx_list] = [date_idx_array.tolist()
for date_idx_array in sparse.find(mstMat)[0:2]]
date12_list = []
for i in range(len(m_idx_list)):
idx = sorted([m_idx_list[i], s_idx_list[i]])
date12 = date6_list[idx[0]]+'-'+date6_list[idx[1]]
date12_list.append(date12)
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_sequential(date_list, num_conn=2, date_format=None):
"""Select Pairs in a Sequential way:
For each acquisition, find its num_connection nearest acquisitions in the past time.
Reference:
Fattahi, H., and F. Amelung (2013), DEM Error Correction in InSAR Time Series, IEEE TGRS, 51(7), 4249-4259.
Parameters: date_list - list of str for date
num_conn - int, number of sequential connections
date_format - str / None, output date format
Returns: date12_list - list of str for date12
"""
date_list = sorted(date_list)
date_inds = list(range(len(date_list)))
# Get pairs index list
date12_inds = []
for date_ind in date_inds:
for i in range(num_conn):
if date_ind - i - 1 >= 0:
date12_inds.append([date_ind - i - 1, date_ind])
date12_inds = [sorted(i) for i in sorted(date12_inds)]
# Convert index into date12
date12_list = [
'{}_{}'.format(date_list[ind12[0]], date_list[ind12[1]])
for ind12 in date12_inds
]
# adjust output date format
if date_format is not None:
if date_format == 'YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
elif date_format == 'YYMMDD':
date12_list = ptime.yymmdd_date12(date12_list)
else:
raise ValueError(
'un-supported date format: {}!'.format(date_format))
return date12_list
def get_mst_date12(keep_mst, par_list_all, date12_list_all, date12_to_drop, min_par, par_name='average coherence'):
"""Get the date12_list of the MST network for the given parameter."""
if keep_mst:
print(f'Get minimum spanning tree (MST) of interferograms with inverse of {par_name}.')
msg = ('Drop ifgrams with '
'1) {} < {} AND '
'2) not in MST network: '.format(par_name, min_par))
# get the current remaining network (after all the above criteria and before data-driven)
date12_to_keep = sorted(list(set(date12_list_all) - set(date12_to_drop)))
par_to_keep = [par for par, date12 in zip(par_list_all, date12_list_all)
if date12 in date12_to_keep]
# get MST from the current remaining network
mst_date12_list = pnet.threshold_coherence_based_mst(date12_to_keep, par_to_keep)
mst_date12_list = ptime.yyyymmdd_date12(mst_date12_list)
else:
msg = 'Drop ifgrams with {} < {}: '.format(par_name, min_par)
mst_date12_list = []
return mst_date12_list, msg
def select_pairs_hierarchical(date_list,
pbase_list,
temp_perp_list,
date12_format='YYMMDD-YYMMDD'):
"""Select Pairs in a hierarchical way using list of temporal and perpendicular baseline thresholds
For each temporal/perpendicular combination, select all possible pairs; and then merge all combination results
together for the final output (Zhao, 2015).
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
pbase_list : list of float, perpendicular spatial baseline
temp_perp_list : list of list of 2 floats, for list of temporal/perp baseline, e.g.
[[32.0, 800.0], [48.0, 600.0], [64.0, 200.0]]
Examples:
pairs = select_pairs_hierarchical(date_list, pbase_list, [[32.0, 800.0], [48.0, 600.0], [64.0, 200.0]])
Reference:
Zhao, W., (2015), Small deformation detected from InSAR time-series and their applications in geophysics, Doctoral
dissertation, Univ. of Miami, Section 6.3.
"""
# Get all date12
date12_list_all = select_pairs_all(date_list)
# Loop of Threshold
print('List of temporal and perpendicular spatial baseline thresholds:')
print(temp_perp_list)
date12_list = []
for temp_perp in temp_perp_list:
tbase_max = temp_perp[0]
pbase_max = temp_perp[1]
date12_list_tmp = threshold_temporal_baseline(date12_list_all,
tbase_max,
keep_seasonal=False)
date12_list_tmp = threshold_perp_baseline(date12_list_tmp, date_list,
pbase_list, pbase_max)
date12_list += date12_list_tmp
date12_list = sorted(list(set(date12_list)))
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def select_pairs_hierarchical(date_list, pbase_list, temp_perp_list, date12_format='YYMMDD-YYMMDD'):
"""Select Pairs in a hierarchical way using list of temporal and perpendicular baseline thresholds
For each temporal/perpendicular combination, select all possible pairs; and then merge all combination results
together for the final output (Zhao, 2015).
Inputs:
date_list : list of date in YYMMDD/YYYYMMDD format
pbase_list : list of float, perpendicular spatial baseline
temp_perp_list : list of list of 2 floats, for list of temporal/perp baseline, e.g.
[[32.0, 800.0], [48.0, 600.0], [64.0, 200.0]]
Examples:
pairs = select_pairs_hierarchical(date_list, pbase_list, [[32.0, 800.0], [48.0, 600.0], [64.0, 200.0]])
Reference:
Zhao, W., (2015), Small deformation detected from InSAR time-series and their applications in geophysics, Doctoral
dissertation, Univ. of Miami, Section 6.3.
"""
# Get all date12
date12_list_all = select_pairs_all(date_list)
# Loop of Threshold
print('List of temporal and perpendicular spatial baseline thresholds:')
print(temp_perp_list)
date12_list = []
for temp_perp in temp_perp_list:
tbase_max = temp_perp[0]
pbase_max = temp_perp[1]
date12_list_tmp = threshold_temporal_baseline(date12_list_all,
tbase_max,
keep_seasonal=False)
date12_list_tmp = threshold_perp_baseline(date12_list_tmp,
date_list,
pbase_list,
pbase_max)
date12_list += date12_list_tmp
date12_list = sorted(list(set(date12_list)))
if date12_format == 'YYYYMMDD_YYYYMMDD':
date12_list = ptime.yyyymmdd_date12(date12_list)
return date12_list
def get_date12_to_drop(inps):
"""Get date12 list to dropped
Return [] if no ifgram to drop, thus KEEP ALL ifgrams;
None if nothing to change, exit without doing anything.
"""
obj = ifgramStack(inps.file)
obj.open()
date12ListAll = obj.date12List
dateList = obj.dateList
print('number of interferograms: {}'.format(len(date12ListAll)))
# Get date12_to_drop
date12_to_drop = []
# reference file
if inps.referenceFile:
date12_to_keep = pnet.get_date12_list(inps.referenceFile, dropIfgram=True)
print('--------------------------------------------------')
print('use reference pairs info from file: {}'.format(inps.referenceFile))
print('number of interferograms in reference: {}'.format(len(date12_to_keep)))
tempList = sorted(list(set(date12ListAll) - set(date12_to_keep)))
date12_to_drop += tempList
print('date12 not in reference file: ({})\n{}'.format(len(tempList), tempList))
# temp baseline threshold
if inps.tempBaseMax:
tempIndex = np.abs(obj.tbaseIfgram) > inps.tempBaseMax
tempList = list(np.array(date12ListAll)[tempIndex])
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with temporal baseline > {} days: ({})\n{}'.format(
inps.tempBaseMax, len(tempList), tempList))
# perp baseline threshold
if inps.perpBaseMax:
tempIndex = np.abs(obj.pbaseIfgram) > inps.perpBaseMax
tempList = list(np.array(date12ListAll)[tempIndex])
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with perp baseline > {} meters: ({})\n{}'.format(
inps.perpBaseMax, len(tempList), tempList))
# connection number threshold
if inps.connNumMax:
seq_date12_list = pnet.select_pairs_sequential(dateList, inps.connNumMax)
seq_date12_list = ptime.yyyymmdd_date12(seq_date12_list)
tempList = [i for i in date12ListAll if i not in seq_date12_list]
date12_to_drop += tempList
print('--------------------------------------------------')
msg = 'Drop ifgrams with temporal baseline beyond {} neighbors: ({})'.format(
inps.connNumMax, len(tempList))
if len(tempList) <= 200:
msg += '\n{}'.format(tempList)
print(msg)
# excludeIfgIndex
if inps.excludeIfgIndex:
tempList = [date12ListAll[i] for i in inps.excludeIfgIndex]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with the following index number: {}'.format(len(tempList)))
for i, date12 in enumerate(tempList):
print('{} : {}'.format(i, date12))
# excludeDate
if inps.excludeDate:
tempList = [i for i in date12ListAll if any(j in inps.excludeDate for j in i.split('_'))]
date12_to_drop += tempList
print('-'*50+'\nDrop ifgrams including the following dates: ({})\n{}'.format(
len(tempList), inps.excludeDate))
print('-'*30+'\n{}'.format(tempList))
# startDate
if inps.startDate:
minDate = int(inps.startDate)
tempList = [i for i in date12ListAll if any(int(j) < minDate for j in i.split('_'))]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with date earlier than: {} ({})\n{}'.format(
inps.startDate, len(tempList), tempList))
# endDate
if inps.endDate:
maxDate = int(inps.endDate)
tempList = [i for i in date12ListAll if any(int(j) > maxDate for j in i.split('_'))]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with date later than: {} ({})\n{}'.format(
inps.endDate, len(tempList), tempList))
# coherence file
if inps.coherenceBased:
print('--------------------------------------------------')
print('use coherence-based network modification')
# get area of interest for coherence calculation
pix_box = get_aoi_pix_box(obj.metadata, inps.lookupFile, inps.aoi_pix_box, inps.aoi_geo_box)
# calculate spatial average coherence
cohList = ut.spatial_average(inps.file,
datasetName='coherence',
maskFile=inps.maskFile,
box=pix_box,
saveList=True)[0]
# get coherence-based network
coh_date12_list = list(np.array(date12ListAll)[np.array(cohList) >= inps.minCoherence])
# get MST network
mst_date12_list, msg = get_mst_date12(inps.keepMinSpanTree, cohList, date12ListAll, date12_to_drop,
min_par=inps.minCoherence,
par_name='average coherence')
# drop all dates (below cohh thresh AND not in MST)
tempList = sorted(list(set(date12ListAll) - set(coh_date12_list + mst_date12_list)))
date12_to_drop += tempList
msg += '({})'.format(len(tempList))
if len(tempList) <= 200:
msg += '\n{}'.format(tempList)
print(msg)
# area ratio file
if inps.areaRatioBased:
print('--------------------------------------------------')
print('use area-ratio-based network modification')
# get area of interest for coherence calculation
pix_box = get_aoi_pix_box(obj.metadata, inps.lookupFile, inps.aoi_pix_box, inps.aoi_geo_box)
# calculate average coherence in masked out areas as threshold
meanMaskCoh = np.nanmean(ut.spatial_average(inps.file,
datasetName='coherence',
maskFile=inps.maskFile,
saveList=True,
reverseMask=True)[0])
print(f'Average coherence of {inps.maskFile} reverse is {meanMaskCoh:.2f}')
# calculate area-ratio with pixels greater than meanMaskCoh
areaRatioList = ut.spatial_average(inps.file,
datasetName='coherence',
maskFile=inps.maskFile,
box=pix_box,
saveList=True,
checkAoi=True,
threshold=meanMaskCoh)[0]
# get area-ratio-based network
area_ratio_date12_list = list(np.array(date12ListAll)[np.array(areaRatioList) >= inps.minAreaRatio])
# get MST network
mst_date12_list, msg = get_mst_date12(inps.keepMinSpanTree, areaRatioList, date12ListAll, date12_to_drop,
min_par=inps.minAreaRatio,
par_name='coherent area ratio')
# drop all dates (below area-ratio thresh AND not in MST)
tempList = sorted(list(set(date12ListAll) - set(area_ratio_date12_list + mst_date12_list)))
date12_to_drop += tempList
msg += '({})'.format(len(tempList))
if len(tempList) <= 200:
msg += '\n{}'.format(tempList)
print(msg)
# Manually drop pairs
if inps.manual:
tempList = manual_select_pairs_to_remove(inps.file)
if tempList is None:
return None
tempList = [i for i in tempList if i in date12ListAll]
print('date12 selected to remove: ({})\n{}'.format(len(tempList), tempList))
date12_to_drop += tempList
## summary
# drop duplicate date12 and sort in order
date12_to_drop = sorted(list(set(date12_to_drop)))
date12_to_keep = sorted(list(set(date12ListAll) - set(date12_to_drop)))
print('--------------------------------------------------')
print('number of interferograms to remove: {}'.format(len(date12_to_drop)))
print('number of interferograms to keep : {}'.format(len(date12_to_keep)))
# print list of date to drop
date_to_keep = [d for date12 in date12_to_keep for d in date12.split('_')]
date_to_keep = sorted(list(set(date_to_keep)))
date_to_drop = sorted(list(set(dateList) - set(date_to_keep)))
if len(date_to_drop) > 0:
print('number of acquisitions to remove: {}\n{}'.format(len(date_to_drop), date_to_drop))
# checking:
# 1) no new date12 to drop against existing file
# 2) no date12 left after dropping
date12ListKept = obj.get_date12_list(dropIfgram=True)
date12ListDropped = sorted(list(set(date12ListAll) - set(date12ListKept)))
if date12_to_drop == date12ListDropped:
print('Calculated date12 to drop is the same as exsiting marked input file, skip updating file.')
date12_to_drop = None
elif date12_to_drop == date12ListAll:
raise Exception('Zero interferogram left! Please adjust your setting and try again.')
return date12_to_drop
def get_date12_to_drop(inps):
"""Get date12 list to dropped
Return [] if no ifgram to drop, thus KEEP ALL ifgrams;
None if nothing to change, exit without doing anything.
"""
obj = ifgramStack(inps.file)
obj.open()
date12ListAll = obj.date12List
dateList = obj.dateList
print('number of interferograms: {}'.format(len(date12ListAll)))
# Get date12_to_drop
date12_to_drop = []
# reference file
if inps.referenceFile:
date12_to_keep = ifgramStack(inps.referenceFile).get_date12_list(dropIfgram=True)
print('--------------------------------------------------')
print('use reference pairs info from file: {}'.format(inps.referenceFile))
print('number of interferograms in reference: {}'.format(len(date12_to_keep)))
tempList = sorted(list(set(date12ListAll) - set(date12_to_keep)))
date12_to_drop += tempList
print('date12 not in reference file: ({})\n{}'.format(len(tempList), tempList))
# coherence file
if inps.coherenceBased:
print('--------------------------------------------------')
print('use coherence-based network modification')
coord = ut.coordinate(obj.metadata, lookup_file=inps.lookupFile)
if inps.aoi_geo_box and inps.lookupFile:
print('input AOI in (lon0, lat1, lon1, lat0): {}'.format(inps.aoi_geo_box))
inps.aoi_pix_box = coord.bbox_geo2radar(inps.aoi_geo_box)
if inps.aoi_pix_box:
inps.aoi_pix_box = coord.check_box_within_data_coverage(inps.aoi_pix_box)
print('input AOI in (x0,y0,x1,y1): {}'.format(inps.aoi_pix_box))
# Calculate spatial average coherence
cohList = ut.spatial_average(inps.file,
datasetName='coherence',
maskFile=inps.maskFile,
box=inps.aoi_pix_box,
saveList=True)[0]
coh_date12_list = list(np.array(date12ListAll)[np.array(cohList) >= inps.minCoherence])
# MST network
if inps.keepMinSpanTree:
print('Get minimum spanning tree (MST) of interferograms with inverse of coherence.')
msg = ('Drop ifgrams with '
'1) average coherence < {} AND '
'2) not in MST network: '.format(inps.minCoherence))
mst_date12_list = pnet.threshold_coherence_based_mst(date12ListAll, cohList)
mst_date12_list = ptime.yyyymmdd_date12(mst_date12_list)
else:
msg = 'Drop ifgrams with average coherence < {}: '.format(inps.minCoherence)
mst_date12_list = []
tempList = sorted(list(set(date12ListAll) - set(coh_date12_list + mst_date12_list)))
date12_to_drop += tempList
msg += '({})'.format(len(tempList))
if len(tempList) <= 200:
msg += '\n{}'.format(tempList)
print(msg)
# temp baseline threshold
if inps.tempBaseMax:
tempIndex = np.abs(obj.tbaseIfgram) > inps.tempBaseMax
tempList = list(np.array(date12ListAll)[tempIndex])
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with temporal baseline > {} days: ({})\n{}'.format(
inps.tempBaseMax, len(tempList), tempList))
# perp baseline threshold
if inps.perpBaseMax:
tempIndex = np.abs(obj.pbaseIfgram) > inps.perpBaseMax
tempList = list(np.array(date12ListAll)[tempIndex])
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with perp baseline > {} meters: ({})\n{}'.format(
inps.perpBaseMax, len(tempList), tempList))
# connection number threshold
if inps.connNumMax:
seq_date12_list = pnet.select_pairs_sequential(dateList, inps.connNumMax)
seq_date12_list = ptime.yyyymmdd_date12(seq_date12_list)
tempList = [i for i in date12ListAll if i not in seq_date12_list]
date12_to_drop += tempList
print('--------------------------------------------------')
msg = 'Drop ifgrams with temporal baseline beyond {} neighbors: ({})'.format(
inps.connNumMax, len(tempList))
if len(tempList) <= 200:
msg += '\n{}'.format(tempList)
print(msg)
# excludeIfgIndex
if inps.excludeIfgIndex:
tempList = [date12ListAll[i] for i in inps.excludeIfgIndex]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with the following index number: {}'.format(len(tempList)))
for i in range(len(tempList)):
print('{} : {}'.format(i, tempList[i]))
#len(tempList), zip(inps.excludeIfgIndex, tempList)))
# excludeDate
if inps.excludeDate:
tempList = [i for i in date12ListAll if any(j in inps.excludeDate for j in i.split('_'))]
date12_to_drop += tempList
print('-'*50+'\nDrop ifgrams including the following dates: ({})\n{}'.format(
len(tempList), inps.excludeDate))
print('-'*30+'\n{}'.format(tempList))
# startDate
if inps.startDate:
minDate = int(inps.startDate)
tempList = [i for i in date12ListAll if any(int(j) < minDate for j in i.split('_'))]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with date earlier than: {} ({})\n{}'.format(
inps.startDate, len(tempList), tempList))
# endDate
if inps.endDate:
maxDate = int(inps.endDate)
tempList = [i for i in date12ListAll if any(int(j) > maxDate for j in i.split('_'))]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with date later than: {} ({})\n{}'.format(
inps.endDate, len(tempList), tempList))
# Manually drop pairs
if inps.manual:
tempList = manual_select_pairs_to_remove(inps.file)
if tempList is None:
return None
tempList = [i for i in tempList if i in date12ListAll]
print('date12 selected to remove: ({})\n{}'.format(len(tempList), tempList))
date12_to_drop += tempList
# drop duplicate date12 and sort in order
date12_to_drop = sorted(list(set(date12_to_drop)))
date12_to_keep = sorted(list(set(date12ListAll) - set(date12_to_drop)))
print('--------------------------------------------------')
print('number of interferograms to remove: {}'.format(len(date12_to_drop)))
print('number of interferograms to keep : {}'.format(len(date12_to_keep)))
date_to_keep = [d for date12 in date12_to_keep for d in date12.split('_')]
date_to_keep = sorted(list(set(date_to_keep)))
date_to_drop = sorted(list(set(dateList) - set(date_to_keep)))
if len(date_to_drop) > 0:
print('number of acquisitions to remove: {}\n{}'.format(len(date_to_drop), date_to_drop))
date12ListKept = obj.get_date12_list(dropIfgram=True)
date12ListDropped = sorted(list(set(date12ListAll) - set(date12ListKept)))
if date12_to_drop == date12ListDropped:
print('Calculated date12 to drop is the same as exsiting marked input file, skip updating file.')
date12_to_drop = None
elif date12_to_drop == date12ListAll:
raise Exception('Zero interferogram left! Please adjust your setting and try again.')
return date12_to_drop
def get_date12_to_drop(inps):
"""Get date12 list to dropped
Return [] if no ifgram to drop, thus KEEP ALL ifgrams;
None if nothing to change, exit without doing anything.
"""
obj = ifgramStack(inps.file)
obj.open()
date12ListAll = obj.date12List
dateList = obj.dateList
print('number of interferograms: {}'.format(len(date12ListAll)))
# Get date12_to_drop
date12_to_drop = []
# reference file
if inps.referenceFile:
date12_to_keep = ifgramStack(
inps.referenceFile).get_date12_list(dropIfgram=True)
print('--------------------------------------------------')
print('use reference pairs info from file: {}'.format(
inps.referenceFile))
print('number of interferograms in reference: {}'.format(
len(date12_to_keep)))
tempList = sorted(list(set(date12ListAll) - set(date12_to_keep)))
date12_to_drop += tempList
print('date12 not in reference file: ({})\n{}'.format(
len(tempList), tempList))
# coherence file
if inps.coherenceBased:
print('--------------------------------------------------')
print('use coherence-based network modification')
coord = ut.coordinate(obj.metadata, lookup_file=inps.lookupFile)
if inps.aoi_geo_box and inps.lookupFile:
print('input AOI in (lon0, lat1, lon1, lat0): {}'.format(
inps.aoi_geo_box))
inps.aoi_pix_box = coord.bbox_geo2radar(inps.aoi_geo_box)
if inps.aoi_pix_box:
inps.aoi_pix_box = coord.check_box_within_data_coverage(
inps.aoi_pix_box)
print('input AOI in (x0,y0,x1,y1): {}'.format(inps.aoi_pix_box))
# Calculate spatial average coherence
cohList = ut.spatial_average(inps.file,
datasetName='coherence',
maskFile=inps.maskFile,
box=inps.aoi_pix_box,
saveList=True)[0]
coh_date12_list = list(
np.array(date12ListAll)[np.array(cohList) >= inps.minCoherence])
# MST network
if inps.keepMinSpanTree:
print(
'Get minimum spanning tree (MST) of interferograms with inverse of coherence.'
)
msg = ('Drop ifgrams with '
'1) average coherence < {} AND '
'2) not in MST network: '.format(inps.minCoherence))
mst_date12_list = pnet.threshold_coherence_based_mst(
date12ListAll, cohList)
mst_date12_list = ptime.yyyymmdd_date12(mst_date12_list)
else:
msg = 'Drop ifgrams with average coherence < {}: '.format(
inps.minCoherence)
mst_date12_list = []
tempList = sorted(
list(set(date12ListAll) - set(coh_date12_list + mst_date12_list)))
date12_to_drop += tempList
msg += '({})'.format(len(tempList))
if len(tempList) <= 200:
msg += '\n{}'.format(tempList)
print(msg)
# temp baseline threshold
if inps.tempBaseMax:
tempIndex = np.abs(obj.tbaseIfgram) > inps.tempBaseMax
tempList = list(np.array(date12ListAll)[tempIndex])
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with temporal baseline > {} days: ({})\n{}'.format(
inps.tempBaseMax, len(tempList), tempList))
# perp baseline threshold
if inps.perpBaseMax:
tempIndex = np.abs(obj.pbaseIfgram) > inps.perpBaseMax
tempList = list(np.array(date12ListAll)[tempIndex])
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with perp baseline > {} meters: ({})\n{}'.format(
inps.perpBaseMax, len(tempList), tempList))
# connection number threshold
if inps.connNumMax:
seq_date12_list = pnet.select_pairs_sequential(dateList,
inps.connNumMax)
seq_date12_list = ptime.yyyymmdd_date12(seq_date12_list)
tempList = [i for i in date12ListAll if i not in seq_date12_list]
date12_to_drop += tempList
print('--------------------------------------------------')
msg = 'Drop ifgrams with temporal baseline beyond {} neighbors: ({})'.format(
inps.connNumMax, len(tempList))
if len(tempList) <= 200:
msg += '\n{}'.format(tempList)
print(msg)
# excludeIfgIndex
if inps.excludeIfgIndex:
tempList = [date12ListAll[i] for i in inps.excludeIfgIndex]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with the following index number: {}'.format(
len(tempList)))
for i in range(len(tempList)):
print('{} : {}'.format(i, tempList[i]))
#len(tempList), zip(inps.excludeIfgIndex, tempList)))
# excludeDate
if inps.excludeDate:
tempList = [
i for i in date12ListAll
if any(j in inps.excludeDate for j in i.split('_'))
]
date12_to_drop += tempList
print('-' * 50 +
'\nDrop ifgrams including the following dates: ({})\n{}'.format(
len(tempList), inps.excludeDate))
print('-' * 30 + '\n{}'.format(tempList))
# startDate
if inps.startDate:
minDate = int(inps.startDate)
tempList = [
i for i in date12ListAll if any(
int(j) < minDate for j in i.split('_'))
]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with date earlier than: {} ({})\n{}'.format(
inps.startDate, len(tempList), tempList))
# endDate
if inps.endDate:
maxDate = int(inps.endDate)
tempList = [
i for i in date12ListAll if any(
int(j) > maxDate for j in i.split('_'))
]
date12_to_drop += tempList
print('--------------------------------------------------')
print('Drop ifgrams with date later than: {} ({})\n{}'.format(
inps.endDate, len(tempList), tempList))
# Manually drop pairs
if inps.manual:
tempList = manual_select_pairs_to_remove(inps.file)
if tempList is None:
return None
tempList = [i for i in tempList if i in date12ListAll]
print('date12 selected to remove: ({})\n{}'.format(
len(tempList), tempList))
date12_to_drop += tempList
# drop duplicate date12 and sort in order
date12_to_drop = sorted(list(set(date12_to_drop)))
date12_to_keep = sorted(list(set(date12ListAll) - set(date12_to_drop)))
print('--------------------------------------------------')
print('number of interferograms to remove: {}'.format(len(date12_to_drop)))
print('number of interferograms to keep : {}'.format(len(date12_to_keep)))
date_to_keep = [d for date12 in date12_to_keep for d in date12.split('_')]
date_to_keep = sorted(list(set(date_to_keep)))
date_to_drop = sorted(list(set(dateList) - set(date_to_keep)))
if len(date_to_drop) > 0:
print('number of acquisitions to remove: {}\n{}'.format(
len(date_to_drop), date_to_drop))
date12ListKept = obj.get_date12_list(dropIfgram=True)
date12ListDropped = sorted(list(set(date12ListAll) - set(date12ListKept)))
if date12_to_drop == date12ListDropped:
print(
'Calculated date12 to drop is the same as exsiting marked input file, skip updating file.'
)
date12_to_drop = None
elif date12_to_drop == date12ListAll:
raise Exception(
'Zero interferogram left! Please adjust your setting and try again.'
)
return date12_to_drop
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
