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aj1365 / ResUNetFormer

This Keras code is for the paper A. Jamali, S. K. Roy, J. Li and P. Ghamisi, "[Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction]," in IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2024.3354560 [https://ieeexplore.ieee.org/document/10400502].
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Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction

Ali Jamali, Swalpa Kumar Roy, Jonathan Li, and Pedram Ghamisi

This Keras code is for the paper A. Jamali, S. K. Roy, J. Li and P. Ghamisi, "[Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction]," in IEEE Geoscience and Remote Sensing Letters, doi: 10.1109/LGRS.2024.3354560 [https://ieeexplore.ieee.org/document/10400502].

Citation

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

  @article{10400502,
          title={Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction},
          author={Jamali, Ali and Roy, Swalpa Kumar and Li, Jonathan and Ghamisi, Pedram},
          journal={IEEE Geoscience and Remote Sensing Letters}, 
          year={2024},
          volume={},
          number={},
          pages={1-5},
          doi={10.1109/LGRS.2024.3354560}
          }

Acknowledgement

Part of the local window attention (LWA) block is implementated from Neighborhood Attention Transformer.

License

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