Spatial–Spectral Self-Attention Learning Network for Defending Against Adversarial Attacks in Hyperspectral Image Classification

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Requirements

• Python 3.7.13
• Pytorch 1.12

Dataset

• Download the Pavia University image and the corresponding annotations. Put these files into the Data folder.

Usage

• Data Preparation:

  • python GenSample.py --train_samples 300

    Prepare the training and testing set. The training samples is generated by randomly selecting 300 samples from each category.

• Adversarial Attack with the FGSM:

  • CUDA_VISIBLE_DEVICES=0 python Attack_FGSM_S3ANet.py --dataID 1 --bins 1 2 3 6 --epoch 1000 --iter 10

• Adversarial Examples Visualization:

  • CUDA_VISIBLE_DEVICES=0 python GenAdvExample.py --model S3ANet --bins 1 2 3 6

Paper

if you find it useful for your research, please consider giving this repo a ⭐ and citing our paper! We appreciate your support！😊

@article{S³ANet,
  title={S³ANet: Spatial–Spectral Self-Attention Learning Network for Defending Against Adversarial Attacks in Hyperspectral Image Classification}, 
  author={Xu, Yichu and Xu, Yonghao and Jiao, Hongzan and Gao, Zhi and Zhang, Lefei},
  journal={IEEE Trans. Geos. Remote Sens.},  
  volume={62},
  pages={1--13},
  year={2024},
}

Acknowledgments

SACNet   FullyContNet   CCNet  

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