.\" DO NOT MODIFY THIS FILE!  It was generated by help2man 1.49.2.
.TH MINTPY-IFGRAM_INVERSION "1" "May 2022" "mintpy-ifgram_inversion v1.3.3" "User Commands"
.SH NAME
mintpy-ifgram_inversion \- Invert network of interferograms into time\-series.
.SH DESCRIPTION
usage: ifgram_inversion.py [\-h] [\-t TEMPLATEFILE] [\-i OBSDATASETNAME]
.TP
[\-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
.PP
Invert network of interferograms into time\-series.
.SS "positional arguments:"
.TP
ifgramStackFile
interferograms stack file to be inverted
.SS "options:"
.TP
\fB\-h\fR, \fB\-\-help\fR
show this help message and exit
.TP
\fB\-t\fR TEMPLATEFILE, \fB\-\-template\fR TEMPLATEFILE
template text file with options
.TP
\fB\-i\fR OBSDATASETNAME, \fB\-d\fR OBSDATASETNAME, \fB\-\-dset\fR OBSDATASETNAME
dataset name of unwrap phase / offset to be used for inversion
e.g.: unwrapPhase, unwrapPhase_bridging, ...
.TP
\fB\-m\fR WATERMASKFILE, \fB\-\-water\-mask\fR WATERMASKFILE
Skip inversion on the masked out region, i.e. water.
.TP
\fB\-o\fR TS_FILE TCOH_FILE NUM_INV_FILE, \fB\-\-output\fR TS_FILE TCOH_FILE NUM_INV_FILE
Output file name. (default: None).
.TP
\fB\-\-ref\-date\fR REF_DATE
Reference date, first date by default.
.TP
\fB\-\-skip\-reference\fR, \fB\-\-skip\-ref\fR
[for offset and testing] do not apply spatial referencing.
.TP
\fB\-\-calc\-cov\fR
Calculate time\-series STD via linear propagation from the network of interferograms or offset pairs.
.TP
\fB\-\-ram\fR MAXMEMORY, \fB\-\-memory\fR MAXMEMORY
Max amount of memory in GB to use (default: 4.0).
Adjust according to your computer memory.
.TP
\fB\-\-update\fR
Enable update mode, and skip inversion if output timeseries file already exists,
readable and newer than input interferograms file
.SS "solver:"
.IP
solver for the network inversion problem
.TP
\fB\-w\fR {fim,coh,var,no}, \fB\-\-weight\-func\fR {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
.TP
\fB\-\-min\-norm\-phase\fR
Enable inversion with minimum\-norm deformation phase, instead of the default minimum\-norm deformation velocity.
.TP
\fB\-\-norm\fR {L1,L2}
Optimization mehtod, L1 or L2 norm. (default: L2).
.SS "mask:"
.IP
mask observation data before inversion
.TP
\fB\-\-mask\-dset\fR MASKDATASET, \fB\-\-mask\-dataset\fR MASKDATASET, \fB\-\-md\fR MASKDATASET
dataset used to mask unwrapPhase, e.g. coherence, connectComponent
.TP
\fB\-\-mask\-thres\fR NUM, \fB\-\-mask\-threshold\fR NUM, \fB\-\-mt\fR NUM
threshold to generate mask when mask is coherence (default: 0.4).
.TP
\fB\-\-min\-redun\fR NUM, \fB\-\-min\-redundancy\fR NUM, \fB\-\-mr\fR NUM
minimum redundancy of interferograms for every SAR acquisition. (default: 1.0).
.SS "parallel:"
.IP
parallel processing using dask
.TP
\fB\-c\fR {lsf,pbs,slurm,local}, \fB\-\-cluster\fR {lsf,pbs,slurm,local}, \fB\-\-cluster\-type\fR {lsf,pbs,slurm,local}
Cluster to use for parallel computing (default: None to turn OFF).
.TP
\fB\-\-num\-worker\fR NUMWORKER
Number of workers to use (default: 4).
.TP
\fB\-\-config\fR CONFIG, \fB\-\-config\-name\fR CONFIG
Configuration name to use in dask.yaml (default: None).
.SS "references:"
.IP
Berardino, P., Fornaro, G., Lanari, R., & Sansosti, E. (2002). A new algorithm for surface
.IP
deformation monitoring based on small baseline differential SAR interferograms. IEEE TGRS,
40(11), 2375\-2383. doi:10.1109/TGRS.2002.803792
.IP
Pepe, A., and Lanari, R. (2006), On the extension of the minimum cost flow algorithm for phase unwrapping
.IP
of multitemporal differential SAR interferograms, IEEE\-TGRS, 44(9), 2374\-2383.
.IP
Perissin, D., and Wang, T. (2012), Repeat\-pass SAR interferometry with partially coherent targets, IEEE TGRS,
.IP
50(1), 271\-280, doi:10.1109/tgrs.2011.2160644.
.IP
Samiei\-Esfahany, S., Martins, J. E., Van Leijen, F., and Hanssen, R. F. (2016), Phase Estimation for Distributed
.IP
Scatterers in InSAR Stacks Using Integer Least Squares Estimation, IEEE TGRS, 54(10), 5671\-5687.
.IP
Seymour, M. S., and Cumming, I. G. (1994), Maximum likelihood estimation for SAR interferometry, 1994.
.IP
IGARSS '94., 8\-12 Aug 1994.
.IP
Yunjun, Z., Fattahi, H., and Amelung, F. (2019), Small baseline InSAR time series analysis: Unwrapping error
.IP
correction and noise reduction, Computers & Geosciences, 133, 104331, doi:10.1016/j.cageo.2019.104331.
.IP
Yunjun, Z., Fattahi, H., Brancato, V., Rosen, P., Simons, M. (2021), Oral: Tectonic displacement mapping from SAR
.IP
offset time series: noise reduction and uncertainty quantification, ID 590, FRINGE 2021, 31 May ??? 4 Jun, 2021, Virtual.
.SS "template options:"
.IP
## 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
.IP
## 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
.IP
## 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.
.SS "example:"
.IP
ifgram_inversion.py inputs/ifgramStack.h5 \fB\-t\fR smallbaselineApp.cfg \fB\-\-update\fR
ifgram_inversion.py inputs/ifgramStack.h5 \fB\-w\fR no  # turn off weight for fast processing
ifgram_inversion.py inputs/ifgramStack.h5 \fB\-c\fR no  # turn off parallel processing
# offset
ifgram_inversion.py inputs/ifgramStack.h5 \fB\-i\fR rangeOffset   \fB\-w\fR no \fB\-m\fR waterMask.h5 \fB\-\-md\fR offsetSNR \fB\-\-mt\fR 5
ifgram_inversion.py inputs/ifgramStack.h5 \fB\-i\fR azimuthOffset \fB\-w\fR no \fB\-m\fR waterMask.h5 \fB\-\-md\fR offsetSNR \fB\-\-mt\fR 5