DeesaSenDT107
network: delaunay
geo_velocity.png
geo_temporalCoherence.png
geo_maskTempCoh.png
geo_avgSpatialCoh.png
network.png
coherenceHistory.png
coherenceMatrix.png
rms_timeseriesResidual_ramp.png
temporalCoherence.png
maskTempCoh.png
avgSpatialCoh.png
maskConnComp.png
numTriNonzeroIntAmbiguity.png
numInvIfgram.png
velocity.png
geometryRadar.png
coherence_1.png
coherence_2.png
coherence_3.png
unwrapPhase_wrap_1.png
unwrapPhase_wrap_2.png
unwrapPhase_wrap_3.png
unwrapPhase_1.png
unwrapPhase_2.png
unwrapPhase_3.png
connectComponent_1.png
connectComponent_2.png
connectComponent_3.png
timeseries_demErr_wrap10_1.png
timeseries_demErr_wrap10_2.png
geo_timeseries_demErr_wrap10_1.png
geo_timeseries_demErr_wrap10_2.png
avgPhaseVelocity.png
pbaseHistory.png
timeseries_wrap10_1.png
timeseries_wrap10_2.png
reference_date.txt
20200327
DeesaSenDT107.template
processor = isce
cleanopt = 0
hazard_products_flag = TRUE
ssaraopt.platform = SENTINEL-1A,SENTINEL-1B
ssaraopt.relativeOrbit = 107
minsar.upload_flag = True # [True / False ], upload to jetstream (Default: False)
minsar.insarmaps_flag = True # [True / False ], ingest into insarmaps (Default: False)
minsar.insarmaps_dataset = PSDS # [PS,DS,PSDS,geo,all], miaplpy dataset to ingest (Default: geo) (MintPy is always geo)
####################
topsStack.boundingBox = 23.9 24.4 68.0 75.0 # '-1 0.15 -91.6 -90.9'
topsStack.subswath = 1 2 # '1 2'
topsStack.numConnections = 3 # comment
topsStack.azimuthLooks = 6 # comment
topsStack.rangeLooks = 24 # comment
topsStack.filtStrength = 0.4 # comment
topsStack.unwMethod = snaphu # comment
topsStack.coregistration = auto # [NESD geometry], auto for NESD
####################
mintpy.load.autoPath = yes
mintpy.troposphericDelay.method = no #[pyaps / height_correlation / base_trop_cor / no], auto for pyaps
mintpy.networkInversion.parallel = no #[yes / no], auto for no, parallel processing
#mintpy.networkInversion.minTempCoh = 0.7 #[0.0-1.0], auto for 0.7, min temporal coherence for mask
mintpy.save.hdfEos5 = yes # [yes / update / no], auto for no, save timeseries to UNAVCO InSAR Archive format
mintpy.save.hdfEos5.subset = yes #[yes / no], auto for no, put subset range info in output filenam
mintpy.save.hdfEos5.update = no # [yes / no], auto for no, put XXXXXXXX as endDate in output filename
mintpy.save.kml = yes # [yes / no], auto for yes, save geocoded velocity to Google Earth KMZ file
mintpy.compute.cluster = local
mintpy.compute.numWorker = 32
####################
#miaplpy.subset.lalo = 24.11:24.223,71.864:71.992
###### DeesaW1 #######
mintpy.subset.lalo = 24.09:24.31,71.79:72.01
miaplpy.subset.lalo = 24.09:24.31,71.79:72.01
minsar.miaplpyDir.addition = DeesaW1 #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
####### DeesaW2 #######
#mintpy.subset.lalo = 23.89:24.11,71.79:72.01
#miaplpy.subset.lalo = 23.89:24.11,71.79:72.01
#minsar.miaplpyDir.addition = DeesaW2 #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
###### DeesaC1 #######
#mintpy.subset.lalo = 24.09:24.31,71.99:72.21
#miaplpy.subset.lalo = 24.09:24.31,71.99:72.21
#minsar.miaplpyDir.addition = DeesaC1 #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
###### DeesaC2 #######
#mintpy.subset.lalo = 23.89:24.11,71.99:72.21
#miaplpy.subset.lalo = 23.89:24.11,71.99:72.21
#minsar.miaplpyDir.addition = DeesaC2 #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
###### DeesaE1 #######
#mintpy.subset.lalo = 24.09:24.31,72.19:72.41
#miaplpy.subset.lalo = 24.09:24.31,72.19:72.41
#minsar.miaplpyDir.addition = DeesaE1 #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
####### DeesaE2 #######
#mintpy.subset.lalo = 23.89:24.11,72.19:72.41
#miaplpy.subset.lalo = 23.89:24.11,72.19:72.41
#minsar.miaplpyDir.addition = DeesaE2 #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
#############################################
miaplpy.load.processor = isce
miaplpy.multiprocessing.numProcessor = 40
miaplpy.inversion.rangeWindow = 24 # range window size for searching SHPs, auto for 15
miaplpy.inversion.azimuthWindow = 9 # azimuth window size for searching SHPs, auto for 15
miaplpy.timeseries.tempCohType = auto # [full, average], auto for full.
miaplpy.timeseries.minTempCoh = auto # auto for 0.5
miaplpy.interferograms.delaunayBaselineRatio = 4 # [1, 4, 9] Ratio between perpendiclar and temporal baselines, auto for 4
miaplpy.interferograms.networkType = single_reference # network
miaplpy.interferograms.networkType = sequential # network
miaplpy.interferograms.networkType = mini_stacks # network
miaplpy.interferograms.networkType = delaunay # network
miaplpy.interferograms.connNum = 8 # network
miaplpy.timeseries.residualNorm = L2 # [L1, L2], auto for L2, norm minimization solution
miaplpy.interferograms.ministackRefMonth = 5 # The month of the year that coherence is high to choose reference from, default: 6
miaplpy.timeseries.residualNorm = auto # [L1, L2], auto for L2, norm minimization solution
#############################################