Self-Attention Context Network for Hyperspectral Image Classification

PyTorch implementation of our method for adversarial attacks and defenses in hyperspectral image classification.

Installation

• Install Pytorch 1.4.0 from https://github.com/pytorch/pytorch with Python 3.7.

• Clone this repo.

git clone https://github.com/YonghaoXu/SACNet

Dataset

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

Usage

• Data Preparation:
  • python GenSample.py
• Adversarial Attack with the FGSM:
  • CUDA_VISIBLE_DEVICES=0 python Attack_FGSM.py
• Adversarial Examples Visualization:
  • CUDA_VISIBLE_DEVICES=0 python GenAdvExample.py

Paper

Self-Attention Context Network: Addressing the Threat of Adversarial Attacks for Hyperspectral Image Classification

Please cite our paper if you find it useful for your research.

@article{sacnet,
  title={Self-Attention Context Network: Addressing the Threat of Adversarial Attacks for Hyperspectral Image Classification}, 
  author={Xu, Yonghao and Du, Bo and Zhang, Liangpei},
  journal={IEEE Transactions on Image Processing}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TIP.2021.3118977}}
}

Acknowledgment

This code is partly borrowed from PyTorch-Encoding

GitHub

View Github