Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing

Jing Yao, Danfeng Hong, Lin Xu, Deyu Meng, Jocelyn Chanussot, Zongben Xu

Run this Matlab code to reproduce the result on Samson Dataset in "Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing".

Citation Information

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

 @article{yao2020sparsity,
  title     = {Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing},
  author    = {J. Yao and D. Hong and L. Xu and D. Meng and J. Chanussot and Z. Xu},
  journal   = {IEEE Trans. Geosci. Remote Sens.}, 
  year      = {2021},
  note      = {DOI:10.1109/TGRS.2021.3069845}
  publisher = {IEEE}
 }

Implementation Details

Before running the main file, please kindly download the Samson dataset from https://rslab.ut.ac.ir/data.

Our code is built on the SPORCO library, owing to Prof. Brendt Wohlberg.

Licensing

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

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

Jing Yao (:incoming_envelope: jasonyao92@gmail.com) is with the School of Mathematics and Statistics, Xi'an Jiaotong University, China;