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