A Sentinel-1 processor based on ESA SNAP and pyroSAR for SLC data producing backscatter intensity and/or InSAR coherence and/or Dual pol H/a decomposition.
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This processing chain is based on the pyroSAR package. It is capable of distinguishing between ascending and descending orbit and can handle multiple relative orbits.
By default, it creates scenes for each selected polarisation and feature (backscatter intensity, InSAR coherence, Dual pol H/a decomposition).
Mosaicking images from several relative orbits has to be done manually.
The backscatter intensity images are geometrically and radiometrically terrain corrected at gamma nought (Ullmann et al. 2019a, 2019b). The output is either linear or dB.
There is also the possibility to process individual bursts and subswaths.
Currently, there are still issues with certain projections/ EPSG-codes that SNAP cannot handle properly.
This section should list any major frameworks/libraries used to bootstrap your project. Leave any add-ons/plugins for the acknowledgements section. Here are a few examples.
To get started, you first need to install ESA SNAP and pyroSAR (GitHub), for which the installation guide can be found here
If you want to use the integrated download function, you also need to register at NASA ASF. We opted for ASF to avoid issues with ESA's rolling archive.
- Clone the repo
git clone https://github.com/eo2cube/s1_processor.git
- Set up your folder structure
##code tbd - Run
setup.py##code tbd - Change git remote url to avoid accidental pushes to base project
git remote set-url origin github_username/repo_name git remote -v # confirm the changes
Add example with carbon gaphics
For more examples, please refer to the Documentation
- Check for potential compability issues with ESA SNAP v11
- Add License
- Create readthedocs.io
See the open issues for a full list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE.txt for more information.
Ullmann, T., Sauerbrey, J., Hoffmeister, D., May, S.M., Baumhauer, R., Bubenzer, O., 2019a. Assessing spatiotemporal variations of sentinel-1 InSAR coherence at different time scales over the atacama desert (Chile) between 2015 and 2018. Remote Sens. 11, 1–22. https://doi.org/10.3390/rs11242960
Ullmann, T., Serfas, K., Büdel, C., Padashi, M., Baumhauer, R., 2019b. Data Processing, Feature Extraction, and Time-Series Analysis of Sentinel-1 Synthetic Aperture Radar (SAR) Imagery: Examples from Damghan and Bajestan Playa (Iran). Zeitschrift für Geomorphol. Suppl. Issues 62, 9–39. https://doi.org/10.1127/zfg_suppl/2019/0524
Johannes Löw - [email protected]
Steven Hill - [email protected]
Project Link: https://github.com/eo2cube/s1_processor