PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-temporal Satellite Imagery

This repository is the official PyTorch implementation of the accepted paper PMAA of ECAI 2023.

Xuechao Zou^1,*, Kai Li^2,*, Junliang Xing², Pin Tao^1,2,†, Yachao Cui¹

Qinghai University¹ • Tsinghua University²

Paper Preprint | Project Page

News

• [2023/07/30] Code release.
• [2023/07/16] PMAA got accepted by ECAI 2023.
• [2023/03/29] PMAA is on arXiv now.

Requirements

To install dependencies:

pip install -r requirements.txt

To download datasets:

• Sen2_MTC_Old: multipleImage.tar.gz

• Sen2_MTC_New: CTGAN.zip

Training

To train the models in the paper, run these commands:

python train_old.py
python train_new.py

Evaluation

To evaluate my models on two datasets, run:

python test_old.py
python test_new.py

Pre-trained Models

You can download pretrained models here:

• Our awesome model trained on Sen2_MTC_old: pmaa_old.pth
• Our awesome model trained on Sen2_MTC_new: pmaa_new.pth

Results

Quantitative Results

Qualitative Results

Citation

If you use our code or models in your research, please cite with:

@article{zou2023pmaa,
  title={PMAA: A Progressive Multi-scale Attention Autoencoder Model for High-Performance Cloud Removal from Multi-temporal Satellite Imagery},
  author={Zou, Xuechao and Li, Kai and Xing, Junliang and Tao, Pin and Cui, Yachao},
  journal={European Conference on Artificial Intelligence (ECAI)},
  year={2023}
}