Cloud removal using SAR and optical images via attention mechanism-based GAN

This repository provides the PyTorch code for our paper “Cloud removal using SAR and optical images via attention mechanism-based GAN”. This code is based on the implementation of Attention-GAN and CycleGAN.

Overview

The pipline of our method:

The result of our method:

Cloud removal results in the real data experiment. Each row corresponds to an example of the results. Column (a) shows the SAR images. Column (b) shows the attention map. Column (c) shows the cloud images. Columns (d) to (h) correspond to the results obtained by the pix2pix model, the SAR-opt-GAN model, the Simulation-Fusion GAN model, the GLF-CR model, and the proposed model. Column (i) shows the ground truth images.

Data

We use SEN12MS-CR dataset in the paper.

You should organize your data into a format like this, replacing them with the data directory in this code:

data
│
└───train/test
│   │
│   └───trainA/testA  #cloud images
│   │   │   1.png
│   │   │   2.png
│   │   │   ...
│   │
│   └───trainB/testB  #cloudless images
│   │   │   1.png
│   │   │   2.png
│   │   │   ...
│   │
│   └───trainC/testC  #SAR images
│   │   │   1.png
│   │   │   2.png
│   │   │   ...
│ 

Environment

You can Install dependencies via:

conda env create --file environment.yml

Train

sh scripts/train.sh

Test

sh scripts/test.sh

Citation

If you find this repository/work helpful in your research, welcome to cite the paper.

@article{zhang2023cloud,
  title={Cloud removal using SAR and optical images via attention mechanism-based GAN},
  author={Zhang, Shuai and Li, Xiaodi and Zhou, Xingyu and Wang, Yuning and Hu, Yue},
  journal={Pattern Recognition Letters},
  volume={175},
  pages={8--15},
  year={2023},
  publisher={Elsevier}
}