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SAR2SAR: a self-supervised despeckling algorithm for SAR images

Based on the work of Emanuele Dalsasso, Loïc Denis, Florence Tupin. Link to Repo

The code is made available under the GNU General Public License v3.0: Copyright 2020, Emanuele Dalsasso, Loïc Denis, Florence Tupin, of LTCI research lab - Télécom ParisTech, an Institut Mines Télécom school. All rights reserved.

Please note that the training set is only composed of Sentinel-1 SAR images, thus this testing code is specific to this data.

How to use the tool

Refer the Wiki for more information.

  1. Preprocess your image into '.npy' file. Check '00_Preprocessing.ipynb'.
  2. Place your processed numpy array under the 'data' directory in the source folder
  3. Run it through the model. Check '01_Interface.ipynb'.
  4. Check for your denoised image under 'output' folder, on sucessful execution

Note: Use the 'test-data' branch to get test-data. The master branch doesn't include any testing data.

Resources

  • Paper (ArXiv) The material is made available under the GNU General Public License v3.0: Copyright 2020, Emanuele Dalsasso, Loïc Denis, Florence Tupin, of LTCI research lab - Télécom ParisTech, an Institut Mines Télécom school. All rights reserved.

To cite the article:

@article{dalsasso2020sar2sar,
    title={{SAR2SAR}: a self-supervised despeckling algorithm for {SAR} images},
    author={Emanuele Dalsasso and Loïc Denis and Florence Tupin},
    journal={arXiv preprint arXiv:2006.15037},
    year={2020}
}