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SACNet

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[IEEE TIP 2021] Self-Attention Context Network: Addressing the Threat of Adversarial Attacks for Hyperspectral Image Classification

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# Self-Attention Context Network for Hyperspectral Image Classification

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

![](https://raw.githubusercontent.com/YonghaoXu/SACNet/main/Figure/SACNet.png)

## 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](http://www.ehu.eus/ccwintco/uploads/e/ee/PaviaU.mat) and the corresponding [annotations](http://www.ehu.eus/ccwintco/uploads/5/50/PaviaU_gt.mat). Put these files into the `Data` folder.

## Usage
* Data Preparation:
  - `python GenSample.py --train_samples 300`

The default training set is generated by randomly selecting `300` samples from each category. 
  
  You can change parameter `--train_samples` to check the performance in other training scenarios.
  
* Performance on the Clean Test Set:
  
  To conduct HSI classification on the original clean test set without adversarial attack, simply run:
  - `CUDA_VISIBLE_DEVICES=0 python Test_Clean.py --model SACNet`

You can also change parameter `--model` to check the performance with other models (e.g., `--model SSFCN`)

* Adversarial Attack with the FGSM:
  - `CUDA_VISIBLE_DEVICES=0 python Attack_FGSM.py --model SACNet`

* Adversarial Examples Visualization:
  - `CUDA_VISIBLE_DEVICES=0 python GenAdvExample.py --model SACNet`

![](https://raw.githubusercontent.com/YonghaoXu/SACNet/main/Figure/AdvExample.png)

## Paper
[Self-Attention Context Network: Addressing the Threat of Adversarial Attacks for Hyperspectral Image Classification](https://ieeexplore.ieee.org/document/9573256/)

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 Trans. Image Process.}, 
  volume={30},
  pages={8671--8685},
  year={2021},
  doi={10.1109/TIP.2021.3118977}}
}
```

## Acknowledgment
This code is partly borrowed from [PyTorch-Encoding](https://github.com/zhanghang1989/PyTorch-Encoding)

SACNet

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