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Cross Hyperspectral and LiDAR Attention Transformer: An Extended Self-Attention for Land Use and Land Cover Classification

Swalpa Kumar Roy, Atri Sukul, Ali Jamali, Juan Mario Haut, and Pedram Ghamisi

The repository contains the implementations for Cross Hyperspectral and LiDAR Attention Transformer: An Extended Self-Attention for Land Use and Land Cover Classification

Sample Dataset

Get the disjoint dataset (<a href="https://drive.google.com/folderview?id=1Wy939ZoRWqIRkPE7NBcndn1LhHTW9HQi" target="_blank">TrentoDataset</a> folder) from Google Drive.

Get the disjoint dataset (<a href="https://drive.google.com/folderview?id=1zn32OnII2DVVeJ2ypMaF71BfLJnlxMu3" target="_blank">HoustonDataset</a> folder) from Google Drive.

Get the disjoint dataset (<a href="https://drive.google.com/folderview?id=1xZx3kMGOc3MmA1GlgaHSTYE0fb3rWc4X" target="_blank">MUUFL_Dataset</a> folder) from Google Drive.

Citation

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

@article{roy2022crosshl,
  title={Cross Hyperspectral and LiDAR Attention Transformer: An Extended Self-Attention for Land Use and Land Cover Classification},
  author={Roy, Swalpa Kumar and Sukul, Atri and Jamali, Ali and Haut, Juan Mario and Ghamisi, Pedram},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume = {},
  year={2024},
  doi = {}
}