Researching | Hyperspectral Abnormal Target Detection Based on Low Rank and Sparse Matrix Decomposition-Sparse Representation

Laser & Optoelectronics Progress, Volume. 56, Issue 4, 042801(2019)

Hyperspectral Abnormal Target Detection Based on Low Rank and Sparse Matrix Decomposition-Sparse Representation

Xiaohui Zhang, Runfang Hao, and Tingyu Li^*

Author Affiliations

College of Information and Computer Science, Taiyuan University of Technology, Taiyuan, Shanxi 030600, China

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References(14)

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[2] Fan L H, Lü J W, Deng J S. Classification of hyperspectral remote sensing images based on bands grouping and classification ensembles[J]. Acta Optica Sinica, 34, 0910002(2014).

[3] Wu Y Q, Zhou Y, Long Y L. Small target detection in hyperspectral remote sensing image based on adaptive parameter SVM[J]. Acta Optica Sinica, 35, 0928001(2015).

[4] Landgrebe D. Hyperspectral image data analysis[J]. IEEE Signal Processing Magazine, 19, 17-28(2002).

[5] Reed I S, Yu X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38, 1760-1770(1990). http://ieeexplore.ieee.org/iel1/29/2187/00060107.pdf

[6] Li J Y, Zhang H Y, Zhang L P et al. Hyperspectral anomaly detection by the use of background joint sparse representation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8, 2523-2533(2015). http://ieeexplore.ieee.org/document/7119558/

[7] Li W, Du Q. Collaborativere presentation for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 1463-1474(2015). http://ieeexplore.ieee.org/document/6876207/

[8] Sun W W, Liu C, Li J L et al. Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery[J]. Journal of Applied Remote Sensing, 8, 083641(2014). http://spie.org/Publications/Journal/10.1117/1.JRS.8.083641

[9] Zhang Y X, Du B, Zhang L P et al. A low-rank and sparse matrix decomposition-based Mahalanobis distance method for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 1376-1389(2016). http://ieeexplore.ieee.org/document/7293169/

[10] Xu Y, Wu Z B, Li J et al. Anomaly detection in hyperspectral images based on low-rank and sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 1990-2000(2016). http://ieeexplore.ieee.org/document/7322257/

[11] Wang Z M, Yang X M, Gu X F. Hyperspectralimage denoising based on tensor group sparse representation[J]. Acta Geodaetica et Cartographica Sinica, 46, 614-622(2017).

[12] Chen Y, Nasrabadi N M, Tran T D. Sparse representation for target detection in hyperspectral imagery[J]. IEEE Journal of Selected Topics in Signal Processing, 5, 629-640(2011). http://ieeexplore.ieee.org/document/5711635/

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[14] Zhang L L, Zhao C H. A spectral-spatial method based on low-rank and sparse matrix decomposition for hyperspectral anomaly detection[J]. International Journal of Remote Sensing, 38, 4047-4068(2017). http://dl.acm.org/citation.cfm?id=3105222

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Xiaohui Zhang, Runfang Hao, Tingyu Li. Hyperspectral Abnormal Target Detection Based on Low Rank and Sparse Matrix Decomposition-Sparse Representation[J]. Laser & Optoelectronics Progress, 2019, 56(4): 042801

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Paper Information

Category: Remote Sensing and Sensors

Received: Jun. 29, 2018

Accepted: Sep. 4, 2018

Published Online: Jul. 31, 2019

The Author Email: Tingyu Li (alffzxh@163.com)

DOI:10.3788/LOP56.042801

Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology