shizhenchang / njcr

Nonnegative-Constrained Joint Collaborative Representation With Union Dictionary for Hyperspectral Anomaly Detection

Nonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection

This is a demo of this work implemented in Matlab, written by Shizhen Chang and Pedram Ghamisi.

For more details, please refer to our paper: Nonnegative-Constrained Joint Collaborative Representation With Union Dictionary for Hyperspectral Anomaly Detection

Environment and package:

• Matlab R2015b
• Ncut

Files

This package contains the following files and directories.

• demo.m: A demo shows how to run this work.
• NJCR.m: Implementation of the NJCR model.
• KNJCR.m: Implementation of the KNJCR model.
• /utils/: Supportive files of this work.

Usage

• After unzipping the files, put the current directory of Matlab to mydir.
• Add the directory path for the dependencies files.

addpath('./utils')
addpath('./Ncut_9/');

• Run demo.m.

Citation

Please cite our paper if you find it is useful for your research.

@article{chang2022Nonnegative,
  author={Chang, Shizhen and Ghamisi, Pedram},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Nonnegative-Constrained Joint Collaborative Representation With Union Dictionary for Hyperspectral Anomaly Detection}, 
  year={2022},
  volume={60},
  number={},
  pages={1-13},
  doi={10.1109/TGRS.2022.3195339}
  }

Acknowledgment

The authors would like to express their thanks to the authors and the creators of Ncut and cluster_dp for releasing their packages.

Please note that if implementing the Ncut_9 in Matlab R2015b, the 81st line of ncut.m should be modified to:

[vbar,s,convergence] = eigs(@mex_w_times_x_symmetric,size(P,1),nbEigenValues,'LA',options,tril(P)); 

License

This demo is distributed under MIT License and is released for scientific purposes only.