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Shuaikai Shi, Min Zhao, Lijun Zhang, Yoann Altmann and Jie Chen, "Probabilistic Generative Model for Hyperspectral Unmixing Accounting for Endmember Variability," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 5516915, doi: 10.1109/TGRS.2021.3121799.

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Probabilistic Generative Model for Hyperspectral Unmixing Accounting for Endmember Variability (PGMSU)

The code in this toolbox implements the "Probabilistic Generative Model for Hyperspectral Unmixing Accounting for Endmember Variability" and "Variational Autoencoders for Hyperspectral Unmixing with Endmember Variability". More specifically, it is detailed as follow.

Train

Run python train.py to train the PGMSU.

Test

Run python test.py to get visual results.

Citation

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

@ARTICLE{shi2021probabilistic,
  author={Shi, Shuaikai and Zhao, Min and Zhang, Lijun and Altmann, Yoann and Chen, Jie},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Probabilistic Generative Model for Hyperspectral Unmixing Accounting for Endmember Variability}, 
  year={2022},
  volume={60},
  number={},
  pages={1-15},
  doi={10.1109/TGRS.2021.3121799}}
  
@INPROCEEDINGS{shi2021variational,
  author={Shi, Shuaikai and Zhao, Min and Zhang, Lijun and Chen, Jie},
  booktitle={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={Variational Autoencoders for Hyperspectral Unmixing with Endmember Variability}, 
  year={2021},
  volume={},
  number={},
  pages={1875-1879},
  doi={10.1109/ICASSP39728.2021.9414940}}

Licensing

Copyright (C) 2021 Shuaikai Shi

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, version 3 of the License.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program.

Contact Information:

If you encounter any bugs while using this code, please do not hesitate to contact us.

Shuaikai Shi _shuaikai_shi@mail.nwpu.edu.cn is with the Center of Intelligent Acoustics and Immersive Communications, School of Marine Science and Technology, Northwestern Polytechinical University, Xi’an 710072, China

About

Shuaikai Shi, Min Zhao, Lijun Zhang, Yoann Altmann and Jie Chen, "Probabilistic Generative Model for Hyperspectral Unmixing Accounting for Endmember Variability," in IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022, Art no. 5516915, doi: 10.1109/TGRS.2021.3121799.

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