The repository contains the implementations for "Global-local Transformer Network for HSI and LiDAR Data Joint Classification". You can find the PDF of this paper.

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Datasets

• MUUFL
• Trento
• Houston

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Train GLT-Net

python demo.py

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Results

Dataset	OA (%)	AA (%)	Kappa (%)
MUUFL	85.23	84.67	80.96
Trento	99.27	98.63	99.03
Houston	95.32	96.01	94.95

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Citation

If you find this paper useful, please cite:

@ARTICLE{9926173,
  author={Ding, Kexing and Lu, Ting and Fu, Wei and Li, Shutao and Ma, Fuyan},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Global–Local Transformer Network for HSI and LiDAR Data Joint Classification}, 
  year={2022},
  volume={60},
  number={},
  pages={1-13},
  doi={10.1109/TGRS.2022.3216319}}
}

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Contact

Kexin Ding: dingkexin@hnu.edu.cn