Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation

This is the matlab code for hyperspectral anomaly detection (LSAD-CR-IDW and LSUNRSORAD algorithms)

For more information of this project, please refer to our paper:

Kun Tan, Zengfu Hou, Fuyu Wu,Qian Du, and Yu Chen. Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation. Remote Sensing 2019. [Co-first author]

Prerequisites

matlab R2018b

Fig.1. AUC values of different window sizes in the Salinas dataset

Citation

If these codes and dataset are helpful for you, please cite this paper:

BibTex Format:

@article{tan2019anomaly,
  title={Anomaly detection for hyperspectral imagery based on the regularized subspace method and collaborative representation},
  author={Tan, Kun and Hou, Zengfu and Wu, Fuyu and Du, Qian and Chen, Yu},
  journal={Remote sensing},
  volume={11},
  number={11},
  pages={1318},
  year={2019},
  publisher={Multidisciplinary Digital Publishing Institute}
}

Plain Text Format:

Tan, K., Hou, Z., Wu, F., Du, Q. and Chen, Y., 2019. Anomaly detection for hyperspectral imagery based on the regularized subspace method and collaborative representation. Remote sensing, 11(11), p.1318.

Other Related Papers

[1] Kun Tan, Zengfu Hou, Dongelei Ma, Yu Chen, and Qian Du. Anomaly detection in hyperspectral imagery based on low-rank representation incorporating a spatial constraint [J]. Remote Sensing, 2019, 11(13): 1578. [Co-first author]

[2] Zengfu Hou, Wei Li, Ran Tao, Pengge Ma, and Weihua Shi. Collaborative Representation with Background Purification and Saliency Weight for Hyperspectral Anomaly Detection [J]. SCIENCE CHINA Information Sciences. 2020.

[3] Jun Liu, Zengfu Hou, Wei Li, Ran Tao, Danilo Orlando and Hongbin Li. Multipixel Anomaly Detection With Unknown Patterns for Hyperspectral Imagery [J]. IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2021.3071026. [Second author]

[4] Zengfu Hou, Wei Li, Lianru Gao, Bing Zhang, Pengge Ma, and Junlin Sun. A BACKGROUND REFINEMENT COLLABORATIVE REPRESENTATION METHOD WITH SALIENCY WEIGHT FOR HYPERSPECTRAL ANOMALY DETECTION [C]. International Geoscience and Remote Sensing, 2020. [Oral]

[5] Zengfu Hou, Yu Chen, Kun Tan, and Peijun Du. NOVEL HYPERSPECTRAL ANOMALY DETECTION METHODS BASED ON UNSUPERVISED NEAREST REGULARIZED SUBSPACE [C]. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 2018, 42(3)

[6] Zengfu Hou, Kun Tan, Yu Chen, and Peijun Du. AN IMPROVED UNSUPERVISED NEAREST REGULARIZED SUBSPACE METHOD FOR HYPERSPECTRAL ANOMALY DETECTION [C]. International Conference on Advanced Remote Sensing, 2018.

Website

1.Github Website: https://zephyrhours.github.io/

2.Chinese CSDN Blog: https://blog.csdn.net/NBDwo

Contact

If you have any other questions, you can send it to my email (See Github Website). I will get back to you as soon as possible!