TangshanSenDT149
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
unwrapPhase_wrap_1.png
unwrapPhase_wrap_2.png
unwrapPhase_1.png
unwrapPhase_2.png
connectComponent_1.png
connectComponent_2.png
avgPhaseVelocity.png
geo_timeseries_demErr_wrap10.png
pbaseHistory.png
timeseries_demErr_wrap10.png
timeseries_wrap10.png
reference_date.txt
20161209
TangshanSenDT149.template
ssaraopt.platform = SENTINEL-1A,SENTINEL-1B
ssaraopt.relativeOrbit = 149
ssaraopt.startDate = 20140101
#ssaraopt.endDate = 20230101
####################
topsStack.boundingBox = 39.0 40.2 116.5 120.0 # '-1 0.15 -91.6 -90.9'
topsStack.subswath = 2 3 # '1 2'
topsStack.subswath = 1 2 3 # '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.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
####################
##### NorthNorthWest #######
minsar.miaplpyDir.addition = NNW #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
miaplpy.subset.lalo = 39.70:39.905,117.79:118.10 #[S:N,W:E / no], auto for no
mintpy.subset.lalo = 39.70:39.905,117.79:118.10 #[S:N,W:E / no], auto for no
###### EastSouthEast #######
#minsar.miaplpyDir.addition = ESE #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
#miaplpy.subset.lalo = 39.295:39.505,118.39:118.66 #[S:N,W:E / no], auto for no
#mintpy.subset.lalo = 39.295:39.505,118.39:118.66 #[S:N,W:E / no], auto for no
####### EastNorthEast #######
#minsar.miaplpyDir.addition = ENE #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
#miaplpy.subset.lalo = 39.495:39.71,118.39:118.66 #[S:N,W:E / no], auto for no
#mintpy.subset.lalo = 39.495:39.71,118.39:118.66 #[S:N,W:E / no], auto for no
###### NorthNorthWest #######
#minsar.miaplpyDir.addition = NNW #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
#miaplpy.subset.lalo = 39.70:39.905,117.79:118.10 #[S:N,W:E / no], auto for no
#mintpy.subset.lalo = 39.70:39.905,117.79:118.10 #[S:N,W:E / no], auto for no
###### NorthNorthEast #######
#minsar.miaplpyDir.addition = NNE #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
#miaplpy.subset.lalo = 39.70:39.905,118.09:118.41 #[S:N,W:E / no], auto for no
#mintpy.subset.lalo = 39.70:39.905,118.09:118.41 #[S:N,W:E / no], auto for no
###### SouthWest #######
#minsar.miaplpyDir.addition = SW #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
#miaplpy.subset.lalo = 39.295:39.505,117.79:118.10 #[S:N,W:E / no], auto for no
#mintpy.subset.lalo = 39.295:39.505,117.79:118.10 #[S:N,W:E / no], auto for no
####### NorthWest #######
#minsar.miaplpyDir.addition = NW #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
#miaplpy.subset.lalo = 39.495:39.71,117.79:118.10 #[S:N,W:E / no], auto for no
#mintpy.subset.lalo = 39.495:39.71,117.79:118.10 #[S:N,W:E / no], auto for no
###### SouthEast #######
#minsar.miaplpyDir.addition = SE #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
#miaplpy.subset.lalo = 39.295:39.505,118.09:118.41 #[S:N,W:E / no], auto for no
#mintpy.subset.lalo = 39.295:39.505,118.09:118.41 #[S:N,W:E / no], auto for no
######## NorthEast #######
#minsar.miaplpyDir.addition = NE #[name / lalo / no ] auto for no (miaply_$name_startDate_endDate))
#miaplpy.subset.lalo = 39.495:39.71,118.09:118.41 #[S:N,W:E / no], auto for no
#mintpy.subset.lalo = 39.495:39.71,118.09:118.41 #[S:N,W:E / no], auto for no
miaplpy.interferograms.referenceDate = auto # auto for the middle image
miaplpy.load.startDate = auto
miaplpy.load.endDate = auto
mintpy.reference.lalo = auto # 25.8248,-80.1222 # auto
mintpy.networkInversion.minTempCoh = 0.80
mintpy.geocode.laloStep = 0.0004,0.0004
miaplpy.timeseries.minTempCoh = 0.7 # auto for 0.5
#############################################
miaplpy.interferograms.networkType = single_reference # network
miaplpy.interferograms.networkType = delaunay # network
#miaplpy.interferograms.delaunayBaselineRatio = 9 # [1, 4, 9] Ratio between perpendiclar and temporal baselines, auto for 4
#miaplpy.interferograms.delaunayTempThresh = 3000 # [days] temporal threshold for delaunay triangles, auto for 120
#miaplpy.interferograms.delaunayPerpThresh = 15 # [meters] Perp baseline threshold for delaunay triangles, auto for 200
#############################################
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
#############################################
insarmaps_flag = False