Provided by: mintpy_1.3.3-2_all 
NAME
mintpy-ifgram_inversion - Invert network of interferograms into time-series.
DESCRIPTION
usage: ifgram_inversion.py [-h] [-t TEMPLATEFILE] [-i OBSDATASETNAME]
[-m WATERMASKFILE]
[-o TS_FILE TCOH_FILE NUM_INV_FILE] [--ref-date REF_DATE] [--skip-reference] [-w
{fim,coh,var,no}] [--min-norm-phase] [--norm {L1,L2}] [--calc-cov] [--mask-dset
MASKDATASET] [--mask-thres NUM] [--min-redun NUM] [--ram MAXMEMORY] [-c
{lsf,pbs,slurm,local}] [--num-worker NUMWORKER] [--config CONFIG] [--update]
ifgramStackFile
Invert network of interferograms into time-series.
positional arguments:
ifgramStackFile
interferograms stack file to be inverted
options:
-h, --help
show this help message and exit
-t TEMPLATEFILE, --template TEMPLATEFILE
template text file with options
-i OBSDATASETNAME, -d OBSDATASETNAME, --dset OBSDATASETNAME
dataset name of unwrap phase / offset to be used for inversion e.g.: unwrapPhase,
unwrapPhase_bridging, ...
-m WATERMASKFILE, --water-mask WATERMASKFILE
Skip inversion on the masked out region, i.e. water.
-o TS_FILE TCOH_FILE NUM_INV_FILE, --output TS_FILE TCOH_FILE NUM_INV_FILE
Output file name. (default: None).
--ref-date REF_DATE
Reference date, first date by default.
--skip-reference, --skip-ref
[for offset and testing] do not apply spatial referencing.
--calc-cov
Calculate time-series STD via linear propagation from the network of interferograms
or offset pairs.
--ram MAXMEMORY, --memory MAXMEMORY
Max amount of memory in GB to use (default: 4.0). Adjust according to your
computer memory.
--update
Enable update mode, and skip inversion if output timeseries file already exists,
readable and newer than input interferograms file
solver:
solver for the network inversion problem
-w {fim,coh,var,no}, --weight-func {fim,coh,var,no}
function used to convert coherence to weight for inversion: var - inverse of phase
variance due to temporal decorrelation (default) fim - Fisher Information Matrix as
weightcoh - spatial coherence no - no/uniform weight
--min-norm-phase
Enable inversion with minimum-norm deformation phase, instead of the default
minimum-norm deformation velocity.
--norm {L1,L2}
Optimization mehtod, L1 or L2 norm. (default: L2).
mask:
mask observation data before inversion
--mask-dset MASKDATASET, --mask-dataset MASKDATASET, --md MASKDATASET
dataset used to mask unwrapPhase, e.g. coherence, connectComponent
--mask-thres NUM, --mask-threshold NUM, --mt NUM
threshold to generate mask when mask is coherence (default: 0.4).
--min-redun NUM, --min-redundancy NUM, --mr NUM
minimum redundancy of interferograms for every SAR acquisition. (default: 1.0).
parallel:
parallel processing using dask
-c {lsf,pbs,slurm,local}, --cluster {lsf,pbs,slurm,local}, --cluster-type
{lsf,pbs,slurm,local}
Cluster to use for parallel computing (default: None to turn OFF).
--num-worker NUMWORKER
Number of workers to use (default: 4).
--config CONFIG, --config-name CONFIG
Configuration name to use in dask.yaml (default: None).
references:
Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new algorithm for
surface
deformation monitoring based on small baseline differential SAR interferograms.
IEEE TGRS, 40(11), 2375-2383. doi:10.1109/TGRS.2002.803792
Pepe, A., and Lanari, R. (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., and Wang, T. (2012), Repeat-pass SAR interferometry with partially
coherent targets, IEEE TGRS,
50(1), 271-280, doi:10.1109/tgrs.2011.2160644.
Samiei-Esfahany, S., Martins, J. E., Van Leijen, F., and Hanssen, R. F. (2016),
Phase Estimation for Distributed
Scatterers in InSAR Stacks Using Integer Least Squares Estimation, IEEE TGRS,
54(10), 5671-5687.
Seymour, M. S., and Cumming, I. G. (1994), Maximum likelihood estimation for SAR
interferometry, 1994.
IGARSS '94., 8-12 Aug 1994.
Yunjun, Z., Fattahi, H., and Amelung, F. (2019), Small baseline InSAR time series
analysis: Unwrapping error
correction and noise reduction, Computers & Geosciences, 133, 104331,
doi:10.1016/j.cageo.2019.104331.
Yunjun, Z., Fattahi, H., Brancato, V., Rosen, P., Simons, M. (2021), Oral: Tectonic
displacement mapping from SAR
offset time series: noise reduction and uncertainty quantification, ID 590, FRINGE
2021, 31 May ??? 4 Jun, 2021, Virtual.
template options:
## Invert network of interferograms into time-series using weighted least sqaure
(WLS) estimator. ## weighting options for least square inversion [fast option
available but not best]: ## a. var - use inverse of covariance as weight (Tough et
al., 1995; Guarnieri & Tebaldini, 2008) [recommended] ## b. fim - use Fisher
Information Matrix as weight (Seymour & Cumming, 1994; Samiei-Esfahany et al.,
2016). ## c. coh - use coherence as weight (Perissin & Wang, 2012) ## d. no -
uniform weight (Berardino et al., 2002) [fast] ## SBAS (Berardino et al., 2002) =
minNormVelocity (yes) + weightFunc (no) mintpy.networkInversion.weightFunc =
auto #[var / fim / coh / no], auto for var mintpy.networkInversion.waterMaskFile
= auto #[filename / no], auto for waterMask.h5 or no [if not found]
mintpy.networkInversion.minNormVelocity = auto #[yes / no], auto for yes, min-norm
deformation velocity / phase mintpy.networkInversion.residualNorm = auto #[L2 ],
auto for L2, norm minimization solution
## mask options for unwrapPhase of each interferogram before inversion (recommend
if weightFunct=no): ## a. coherence - mask out pixels with spatial
coherence < maskThreshold ## b. connectComponent - mask out pixels with
False/0 value ## c. no - no masking [recommended]. ## d.
range/azimuthOffsetStd - mask out pixels with offset std. dev. > maskThreshold [for
offset] mintpy.networkInversion.maskDataset = auto #[coherence / connectComponent
/ rangeOffsetStd / azimuthOffsetStd / no], auto for no
mintpy.networkInversion.maskThreshold = auto #[0-inf], auto for 0.4
mintpy.networkInversion.minRedundancy = auto #[1-inf], auto for 1.0, min num_ifgram
for every SAR acquisition
## Temporal coherence is calculated and used to generate the mask as the
reliability measure ## reference: Pepe & Lanari (2006, IEEE-TGRS)
mintpy.networkInversion.minTempCoh = auto #[0.0-1.0], auto for 0.7, min temporal
coherence for mask mintpy.networkInversion.minNumPixel = auto #[int > 1], auto for
100, min number of pixels in mask above mintpy.networkInversion.shadowMask = auto
#[yes / no], auto for yes [if shadowMask is in geometry file] or no.
example:
ifgram_inversion.py inputs/ifgramStack.h5 -t smallbaselineApp.cfg --update
ifgram_inversion.py inputs/ifgramStack.h5 -w no # turn off weight for fast
processing ifgram_inversion.py inputs/ifgramStack.h5 -c no # turn off parallel
processing # offset ifgram_inversion.py inputs/ifgramStack.h5 -i rangeOffset -w
no -m waterMask.h5 --md offsetSNR --mt 5 ifgram_inversion.py inputs/ifgramStack.h5
-i azimuthOffset -w no -m waterMask.h5 --md offsetSNR --mt 5