UCGAN

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This is the official implementation of "Unsupervised Cycle-consistent Generative Adversarial Networks for Pan-sharpening". The paper is accepted to TGRS2022.

Overview of UCGAN

Architecture of UCGAN

Requirements

This environment is mainly based on python=3.6 with CUDA=10.2

conda create -n ucgan python=3.6
conda activate ucgan
conda install pytorch=1.7.1 torchvision=0.2.2 cudatoolkit=10.2
pip install mmcv==1.2.7
conda install gdal=3.1.0 -c conda-forge
conda install scikit-image=0.17.2
pip install scipy==1.5.3
pip install gpustat==0.6.0
pip install numba==0.53.1 
pip install einops==0.3.0 
pip install timm==0.3.2
pip install sewar==0.4.4

Test with the pretrained Model

Due to the large size of the dataset, we only provide some samples in './data' to verify the code.

conda activate ucgan
export CUDA_VISIBLE_DEVICES='0';
python main.py -c configs/ucgan.py

You can modify the config file 'configs/ucgan.py' for different purposes.

Citing UCGAN

Consider cite UCGAN in your publications if it helps your research.

@article{zhou2022unsupervised,
  title={Unsupervised Cycle-Consistent Generative Adversarial Networks for Pan Sharpening},
  author={Zhou, Huanyu and Liu, Qingjie and Weng, Dawei and Wang, Yunhong},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  volume={60},
  pages={1--14},
  year={2022},
  publisher={IEEE}
}