/Hyperspectral-Anomaly-Detection-LSUNRSORAD-and-LSAD-CR-IDW-

This is the code for the paper nemed 'Anomaly Detection for Hyperspectral Imagery Based on the Regularized Subspace Method and Collaborative Representation'

Primary LanguageMATLAB

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

WinSize_Salinas

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!