hanzhu97702 / ISPRS_STMA

Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery, ISPRS Journal of Photogrammetry and Remote Sensing

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Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery

Zhu Han, Ce Zhang, Lianru Gao, Zhiqiang Zeng, Bing Zhang, Peter M. Atkinson


This is a PyTorch implementation of the "Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery" in ISPRS Journal of Photogrammetry and Remote Sensing. More specifically, it is detailed as follow.

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Usage

Dataset

The adopted Brandenburg Sentinel-1 time-series dataset can be downloaded in Baiduyun Link or Google Drive Link.

The specific class information of this dataset is listed as follows:

0:Background
1: Maize
2: Wheat
3: Grassland
4: Peanut
5: Potato
6:Residue
7: Fallow
8: Rapeseed
9: Vegetable
10: Legume
11: Herb
12: Orchard
13: Flower
14: Sugar beet
15:Other

Citation

Please kindly cite the papers if this code is useful and helpful for your research.

Zhu Han, Ce Zhang, Lianru Gao, Zhiqiang Zeng, Bing Zhang, Peter M. Atkinson. Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 206, pp. 293-310, 2023, doi: 10.1016/j.isprsjprs.2023.11.01.

@article{HAN2023293,
  title = {Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery},
  author = {Zhu Han and Ce Zhang and Lianru Gao and Zhiqiang Zeng and Bing Zhang and Peter M. Atkinson},
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume = {206},
  pages = {293-310},
  year = {2023},
  issn = {0924-2716},
  doi = {https://doi.org/10.1016/j.isprsjprs.2023.11.016},
  url = {https://www.sciencedirect.com/science/article/pii/S0924271623003210},
}

Contact Information

Zhu Han: hanzhu19@mails.ucas.ac.cn

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Spatio-temporal multi-level attention crop mapping method using time-series SAR imagery, ISPRS Journal of Photogrammetry and Remote Sensing


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Language:Python 100.0%