Hyperspectral-Anomaly-Detection-SLW_LRRSTO-MLW_LRRSTO-

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Hyperspectral-Anomaly-Detection-SLW_LRRSTO-MLW_LRRSTO-

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This is the code for the paper named "Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint"

Hyperspectral-Anomaly-Detection-SLW_LRRSTO-MLW_LRRSTO- 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Hyperspectral Anomaly Detection: SLW_LRRSTO/MLW_LRRSTO
This is the code for the paper named "Anomaly Detection in Hyperspectral Imagery Based on Low-Rank Representation Incorporating a Spatial Constraint"
For more information of this project, please refer to our paper:

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.](https://www.mdpi.com/2072-4292/11/13/1578) [Co-first author]

<img src="Flowchat_SLW.png" alt="Flowchat SLW">
Fig.1. Schematic diagram of a single local window and multiple local windows and the corresponding filters (N = 5): (a) single local window; (b) multiple local windows.

## Prerequisites
MATLAB R2018b

## Statement
- To accelerate the operation, in the proposed SLW_LRRSTO/MLW_LRRSTO models, random selection method was adopted to construct initial dictionary. Hence, the result has a certain randomness. 
- It is worth noting that the detection results of all the methods in this paper are the average value of 10 repeated experiments.
- The dictionary in the shared demo program is a random dictionary, which is only used for program testing.

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

**BibTex Format:**<br />
```
@article{tan2019anomaly,
  title={Anomaly detection in hyperspectral imagery based on low-rank representation incorporating a spatial constraint},
  author={Tan, Kun and Hou, Zengfu and Ma, Donglei and Chen, Yu and Du, Qian},
  journal={Remote Sensing},
  volume={11},
  number={13},
  pages={1578},
  year={2019},
  publisher={Multidisciplinary Digital Publishing Institute}
}
```

**Plain Text Format:**<br />
```
Tan, K., Hou, Z., Ma, D., Chen, Y. and Du, Q., 2019. Anomaly detection in hyperspectral imagery based on low-rank representation incorporating a spatial constraint. Remote Sensing, 11(13), p.1578.
```

# Other Anomaly Detection Papers
[1] 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[J]. Remote Sensing, 2019, 11(11): 1318.](https://www.mdpi.com/2072-4292/11/11/1318) [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](https://link.springer.com/article/10.1007/s11432-020-2915-2) [J]. Science China Information Sciences, 2022 Jan, 65(1):1-12. doi: https://doi.org/10.1007/s11432-020-2915-2.

[3] Jun Liu, **Zengfu Hou**, Wei Li, Ran Tao, Danilo Orlando and Hongbin Li. [Multipixel Anomaly Detection With Unknown Patterns for Hyperspectral Imagery](https://ieeexplore.ieee.org/abstract/document/9404853) [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](https://ieeexplore.ieee.org/abstract/document/9324521) [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](https://pdfs.semanticscholar.org/43ba/4ca04679165d72e9f3fd3a9b1346437b81f9.pdf) [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](https://drive.google.com/file/d/1g3KkwsN2Q8pk7aMZEKGRdfoGEEcOTTRp/view) [C]. International Conference on Advanced Remote Sensing, 2018.

[7] **Zengfu Hou**, Siyuan Cheng, and Ting Hu. A Spectral-Spatial Fusion Anomaly Detection Method for Hyperspectral Imagery [J]. arxiv.org/abs/2202.11889. 2022. [Preprint Paper](https://arxiv.org/abs/2202.11889)

# Website
1.Github Website: [https://zephyrhours.github.io/](https://zephyrhours.github.io/)

2.Google Scholar: [https://scholar.google.com/citations?user=FsNcDUEAAAAJ&hl=en&oi=ao](https://scholar.google.com/citations?user=FsNcDUEAAAAJ&hl=en&oi=ao)

3.Chinese CSDN Blog: [https://blog.csdn.net/NBDwo](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!

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