Provided by: mintpy_1.3.3-2_all
NAME
mintpy-spatial_filter - Spatial filtering of 2D image.
DESCRIPTION
usage: spatial_filter.py [-h] [-f {lowpass_gaussian,highpass_gaussian,lowpass_avg,highpass_avg,sobel,roberts,canny,double_difference}] [-p [FILTER_PAR ...]] [-o OUTFILE] file [dset ...] Spatial filtering of 2D image. positional arguments: file File to be filtered dset optional - dataset(s) to filter (default: []). options: -h, --help show this help message and exit -f {lowpass_gaussian,highpass_gaussian,lowpass_avg,highpass_avg,sobel,roberts,canny,double_difference} Filter type (default: lowpass_gaussian). Check Bekaert et al. (2020) for double_difference; Check scikit-image as below for the other filters: http://scikit-image.org/docs/dev/api/skimage.filters.html -p [FILTER_PAR ...], --filter_par [FILTER_PAR ...] Filter parameters for filters. Default: Sigma for low/high pass gaussian filter, default: 3.0 Kernel Size for low/high pass average filter, default: 5 Kernel Radius for double difference local and regional filters, default: 1 10 -o OUTFILE, --outfile OUTFILE Output file name. references: Bekaert, David PS, et al. "InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal." Remote Sensing of Environment 249 (2020), doi:10.1016/j.rse.2020.111983. example: spatial_filter.py velocity.h5 spatial_filter.py timeseries.h5 -f lowpass_avg -p 5 spatial_filter.py velocity.h5 -f lowpass_avg -p 5 spatial_filter.py velocity.h5 -f highpass_gaussian -p 3 spatial_filter.py velocity.h5 -f sobel spatial_filter.py ifgramStack.h5 unwrapPhase spatial_filter.py ifgramStack.h5 unwrapPhase -f lowpass_avg -p 5 spatial_filter.py ifgramStack.h5 unwrapPhase -f double_difference -p 1 10