70 lines
4.8 KiB
INI
70 lines
4.8 KiB
INI
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# vim: set filetype=cfg:
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##------------------------------- ISCE/topsStack ----------------------##
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##-------------------------------- MintPy -----------------------------##
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########## 1. Load Data (--load to exit after this step)
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## load_data.py -H to check more details and example inputs.
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mintpy.load.processor = isce
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##---------for ISCE only:
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mintpy.load.metaFile = isce_work_space/reference/IW*.xml
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mintpy.load.baselineDir = isce_work_space/baselines
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##---------interferogram datasets:
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mintpy.load.unwFile = isce_work_space/merged/interferograms/*/filt_*.unw
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mintpy.load.corFile = isce_work_space/merged/interferograms/*/filt_*.cor
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mintpy.load.connCompFile = isce_work_space/merged/interferograms/*/filt_*.unw.conncomp
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##---------geometry datasets:
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mintpy.load.demFile = isce_work_space/merged/geom_reference/hgt.rdr
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mintpy.load.lookupYFile = isce_work_space/merged/geom_reference/lat.rdr
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mintpy.load.lookupXFile = isce_work_space/merged/geom_reference/lon.rdr
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mintpy.load.incAngleFile = isce_work_space/merged/geom_reference/los.rdr
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mintpy.load.azAngleFile = isce_work_space/merged/geom_reference/los.rdr
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mintpy.load.shadowMaskFile = isce_work_space/merged/geom_reference/shadowMask.rdr
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##---------multilook (optional):
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## multilook while loading data with nearest interpolation, to reduce dataset size
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mintpy.load.ystep = auto #[int >= 1], auto for 1 - no multilooking
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mintpy.load.xstep = auto #[int >= 1], auto for 1 - no multilooking
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##---------subset (optional):
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## if both yx and lalo are specified, use lalo option unless a) no lookup file AND b) dataset is in radar coord
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mintpy.subset.yx = auto #[y0:y1,x0:x1 / no], auto for no
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mintpy.subset.lalo = auto #[S:N,W:E / no], auto for no
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########## 2. modify_network
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########## 3. reference_point
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mintpy.reference.maskFile = no #[filename / no], auto for maskConnComp.h5
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mintpy.reference.minCoherence = auto #[0.0-1.0], auto for 0.85, minimum coherence for auto method
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########## 4. correct_unwrap_error (optional)
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## a. phase_closure - suitable for highly redundant network
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## b. bridging - suitable for regions separated by narrow decorrelated features, e.g. rivers, narrow water bodies
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## c. bridging+phase_closure - recommended when there is a small percentage of errors left after bridging
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mintpy.unwrapError.method = no #[bridging / phase_closure / bridging+phase_closure / no], auto for no
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mintpy.unwrapError.waterMaskFile = no #[waterMask.h5 / no], auto for waterMask.h5 or no [if not found]
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########## 5. invert_network
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mintpy.networkInversion.waterMaskFile = no #[filename / no], auto for waterMask.h5 or no [if not found]
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mintpy.networkInversion.maskDataset = no #[coherence / connectComponent / offsetSNR / no], auto for no
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## Temporal coherence is calculated and used to generate the mask as the reliability measure
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## reference: Pepe & Lanari (2006, IEEE-TGRS)
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mintpy.networkInversion.shadowMask = auto #[yes / no], auto for yes [if shadowMask is in geometry file] or no.
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########## 6. correct_troposphere (optional but recommended)
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mintpy.troposphericDelay.method = height_correlation #[pyaps / height_correlation / gacos / no], auto for pyaps
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## Notes for height_correlation:
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## Extra multilooking is applied to estimate the empirical phase/elevation ratio ONLY.
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## For an dataset with 5 by 15 looks, looks=8 will generate phase with (5*8) by (15*8) looks
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## to estimate the empirical parameter; then apply the correction to original phase (with 5 by 15 looks),
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## if the phase/elevation correlation is larger than minCorrelation.
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mintpy.troposphericDelay.polyOrder = auto #[1 / 2 / 3], auto for 1
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mintpy.troposphericDelay.looks = auto #[1-inf], auto for 8, extra multilooking num
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mintpy.troposphericDelay.minCorrelation = auto #[0.0-1.0], auto for 0
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########## 7. deramp (optional)
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########## 8. correct_topography (optional but recommended)
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########## 9.1 residual_RMS (root mean squares for noise evaluation)
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########## 9.2 reference_date
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mintpy.reference.date = auto #[reference_date.txt / 20090214 / no], auto for reference_date.txt
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########## 10. velocity
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## Estimate linear velocity and its standard deviation from time-series
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## and from tropospheric delay file if exists.
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## reference: Fattahi and Amelung (2015, JGR)
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mintpy.velocity.excludeDate = auto #[exclude_date.txt / 20080520,20090817 / no], auto for exclude_date.txt
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mintpy.velocity.startDate = auto #[20070101 / no], auto for no
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mintpy.velocity.endDate = auto #[20101230 / no], auto for no
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## Bootstrapping
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## refernce: Efron and Tibshirani (1986, Stat. Sci.)
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mintpy.velocity.bootstrap = auto #[yes / no], auto for no, use bootstrap
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mintpy.velocity.bootstrapCount = auto #[int>1], auto for 400, number of iterations for bootstrapping
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