Dynamic Surface Water Extent from Synthetic Aperture Radar
Download the source code and change working directory to cloned repository:
git clone https://github.com/opera-adt/DSWX-SAR.git
Install dependencies (installation via conda is recommended):
conda install --file docker/requirements.txt
conda install -c conda-forge --file docker/requirements_forge.txt
Install via setup.py:
python setup.py install
python setup.py clean
Note: Installation via pip is not currently recommended due to an issue with the osgeo and gdal dependency.
OR update environment path to run DSWX-SAR:
export DSWX_SAR_HOME=$PWD
export PYTHONPATH=${PYTHONPATH}:${DSWX_SAR_HOME}/src
export PATH=${PATH}:${DSWX_SAR_HOME}/bin
Process data sets; use a runconfig file to specify the location of the dataset, the output directory, parameters, etc.
dswx_s1.py <path to runconfig file>
Note: Only Sentinel-1 data is currently supported.
A default runconfig file can be found: DSWX-SAR > src > dswx_sar > defaults > dswx_s1.yaml
.
This file can be copied and modified for your needs.
Note: The runconfig must meet this schema: DSWX-SAR > src > dswx_sar > schemas > dswx_s1.yaml
.
Skip the standard installation process above.
Then, from inside the cloned repository, build the Docker image: (This will automatically run the workflow tests.)
./build_docker_image.sh
Load the Docker container image onto your computer:
docker load -i docker/dockerimg_dswx_s1_calval_0.4.2.tar
See DSWx-SAR Science Algorithm Software (SAS) User Guide for instructions on processing via Docker.
Copyright (c) 2021 California Institute of Technology (“Caltech”). U.S. Government sponsorship acknowledged.
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