Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing
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".
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}
}
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.
Copyright (C) 2020 Jing Yao and Danfeng Hong
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.
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;
Danfeng Hong (:incoming_envelope: hongdanfeng1989@gmail.com) is with the Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Germany, and also with the Singnal Processing in Earth Oberservation (SiPEO), Technical University of Munich (TUM), Germany.