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MSTT-STVSR (JAG 2022)

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PyTorch codes for "Space-time Super-resolution for Satellite Video: A Joint Framework Based on Multi-Scale Spatial-Temporal Transformer", International Journal of Applied Earth Observation and Geoinformation (JAG), 2022.

Authors: Yi Xiao, Qiangqiang Yuan*, Jiang He, Qiang Zhang, Jing Sun, Xin Su, Jialian Wu, and Liangpei Zhang<br> Wuhan University

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Abstract

Satellite video is an emerging type of earth observation tool, which has attracted increasing attention because of its application in dynamic analysis. However, most studies only focus on improving the spatial resolution of satellite video imagery. In contrast, few works are committed to enhancing the temporal resolution, and the joint spatial-temporal improvement is even less. The joint spatial-temporal enhancement can not only produce high-resolution imagery for subsequent applications, but also provide the potentials of clear motion dynamics for extreme events observation. In this paper, we propose a joint framework to enhance the spatial and temporal resolution of satellite video simultaneously. Firstly, to alleviate the problem of scale variation and scarce motion in satellite video, we design a feature interpolation module that deeply couples optical flow and multi-scale deformable convolution to predict unknown frames. Deformable convolution can adaptively learn the multi-scale motion information and profoundly complement optical flow information. Secondly, a multi-scale spatial-temporal transformer is proposed to aggregate the contextual information in long-time series video frames effectively. Since multi-scale patches are embedded in multiple heads for spatial-temporal self-attention calculation, we can comprehensively exploit multi-scale details in all frames. Extensive experiments on the Jilin-1 satellite video demonstrate that our model is superior to the existing methods. The source code is available at https://github.com/XY-boy.

Overall

image

Environment

Dataset Preparation

We reorganize the satellite video super-resolution data set named Jilin-189 proposed in our previous work MSDTGP to ensure the data directory structure is consistent with the Vimeo-90K. Finally, We obtained 2,647 video clips as a training set.

Please download our dataset 189_vime7 from Baidu Netdisk. Code: 0rc2

You can also train your dataset following the directory sturture below!

Data directory structure

trainset--
 | 189_vime7--
  | train---
   | 000.png
   | Β·Β·Β·.png
   | 006.png

Training

python main.py

Test

python test.py

Quantitative results

image

Qualitative results

image More details can be found in our paper!

Contact

If you have any questions or suggestions, feel free to contact me.
Email: xiao_yi@whu.edu.cn
Tel: (+86) 15927574475 (WeChat)

Citation

If you find our work helpful, please consider citing it. Thank you very much! (っ‒̀ω‒́)っβ₯β₯β₯β₯⁾⁾

@article{xiao2022mstt,
  title={Space-time super-resolution for satellite video: A joint framework based on multi-scale spatial-temporal transformer},
  author={Xiao, Yi and Yuan, Qiangqiang and He, Jiang and Zhang, Qiang and Sun, Jing and Su, Xin and Wu, Jialian and Zhang, Liangpei},
  journal={International Journal of Applied Earth Observation and Geoinformation},
  volume={108},
  pages={102731},
  year={2022},
  publisher={Elsevier}
}

Acknowledgement

Our work is built upon our previous work MSDTGP and STTN.
Thanks to the author for the source code!