Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection

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

For more details, please refer to our paper: Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection

Environment:

• Matlab R2015b

Files

The package contains the following files.

• demo.m: A demo shows how to run this work.
• SMSL_ACD.m: Implementation of the SMSL model.
• jlt.m: Calculate the sketched dictionary through JLT random projection.
• meanjlt.m: Calculate the mean sketched dictionary after repeating JLT random projection.
• roc_i.m: Calculate the ROC curve.
• hyperNormalize.m: Supportive files to normalize the data.

Usage

• After unzipping the files, put the current directory of Matlab to mydir.
• Run demo.m.

Citation

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

@article{chang2022sketched,
  author={Chang, Shizhen and Kopp, Michael and Ghamisi, Pedram},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={Sketched Multi-view Subspace Learning for Hyperspectral Anomalous Change Detection}, 
  year={2022},
  volume={},
  number={},
  pages={1-13},
  doi={10.1109/TGRS.2022.3220814}
  }

Acknowledgment

The authors would like to express their thanks to the creators of Viareggio and BGU-iCVL-hyperspectral-image datasets.

Notice

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