Residual wave vision U-Net for flood mapping using dual polarization Sentinel-1 SAR imagery☆

Ali Jamali, Swalpa Kumar Roy, Leila Hashemi Beni, Biswajeet Pradhan, Jonathan Li, and Pedram Ghamisi

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Citation

Please kindly cite the paper if this code is useful and helpful for your research.

   @article{JAMALI2024103662,
           title = {Residual wave vision U-Net for flood mapping using dual polarization Sentinel-1 SAR imagery},
           author = {Ali Jamali and Swalpa Kumar Roy and Leila Hashemi Beni and Biswajeet Pradhan and Jonathan Li and Pedram Ghamisi},
           journal = {International Journal of Applied Earth Observation and Geoinformation},
           volume = {127},
           pages = {103662},
           year = {2024},
           issn = {1569-8432},
           doi = {https://doi.org/10.1016/j.jag.2024.103662},
           url = {https://www.sciencedirect.com/science/article/pii/S1569843224000165},
          }

Acknowledgement

Wave Vision MLP is adopted from (https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/wavemlp_pytorch).

Weights

https://drive.google.com/file/d/1rlNmu4GtYuyukXpVzVVbRnLFimdrSSCN/view?usp=sharing

License

Copyright (c) 2023 Ali Jamali. Released under the MIT License. See LICENSE for details.