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Journals >Laser & Optoelectronics Progress >Volume 55 >Issue 12 >Page 122801 > Article

Laser & Optoelectronics Progress
Vol. 55, Issue 12, 122801 (2018)

Hyperspectral Anomaly Detection Algorithm Based on Combination of Spectral and Spatial Information

Huihui Ju, Zhigang Liu^*, and Yang Wang

Author Affiliations

Institute of Nuclear Engineering, Rocket Force Engineering University, Xi'an, Shaanxi 710025, China

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DOI: 10.3788/LOP55.122801 Cite this Article Set citation alerts

Huihui Ju, Zhigang Liu, Yang Wang. Hyperspectral Anomaly Detection Algorithm Based on Combination of Spectral and Spatial Information[J]. Laser & Optoelectronics Progress, 2018, 55(12): 122801 Copy Citation Text

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References

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[4] Ranney K I, Soumekh M. Hyperspectral anomaly detection within the signal subspace[J]. Proceedings of the IEEE, 3, 312-316(2006). http://ieeexplore.ieee.org/document/1657995/

[5] Li W, Du Q. Collaborative representation for hyperspectral anomaly detection[J]. Proceedings of the IEEE, 53, 1463-1474(2015). http://ieeexplore.ieee.org/document/6876207/

[6] 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]. Proceedings of the IEEE, 54, 1376-1389(2016). http://ieeexplore.ieee.org/document/7293169/

[7] 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

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

[9] Cheng B Z, Zhao C H, Zhang L L et al. Joint spatial preprocessing and spectral clustering based collaborative sparsity anomaly detection for hyperspectral images[J]. Acta Optica Sinica, 37, 0428001(2017).

[10] Kang X D, Zhang X P, Li S T. et al. Hyperspectral anomaly detection with attribute and edge-preserving filters[J]. Proceedings of the IEEE, 55, 5600-5611(2017). http://ieeexplore.ieee.org/document/7994698/

[11] Zhang X, Wen G J, Dai W. A tensor decomposition-based anomaly detection algorithm for hyperspectral image[J]. Proceedings of the IEEE, 54, 5801-5820(2016). http://ieeexplore.ieee.org/document/7493677

[12] Li N, Zhao H J, Jia G R et al. Anomaly detection based on extended mathematical morphology for hyperspectral imagery[J]. Acta Optica Sinica, 28, 1480-1484(2008).

[13] Liao J S, Wang L G. Hyperspectral image classification method based on fusion with two kinds of spatial information[J]. Laser & Optoelectronics Progress, 54, 081002(2017).

[14] Ma Y P, Zhang W, Liu D X. Characteristics of hyperspectral reconnaissance and threat to ground military targets[J]. Aerospace Shanghai, 29, 37-40(2012).

[15] Sun W F. Research on signal and image processing algorithms based on non-local information and their applications[D]. Jinan: Shandong University, 51-60(2010).

[16] Efros A A, Leung T L. Texture synthesis by non-parametric sampling[J]. Proceedings of the IEEE, 2, 1033-1038(2002). http://doi.ieeecomputersociety.org/resolve?ref_id=doi:10.1109/ICCV.1999.790383&rfr_id=trans/tp/2004/06/ttp2004060771.htm

[17] Su P F, Liu D Z, Li X H et al. A saliency-based band selection approach for hyperspectral imagery inspired by scale selection[J]. Proceedings of the IEEE, 15, 572-576(2018). http://ieeexplore.ieee.org/document/8306265/

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Huihui Ju, Zhigang Liu, Yang Wang. Hyperspectral Anomaly Detection Algorithm Based on Combination of Spectral and Spatial Information[J]. Laser & Optoelectronics Progress, 2018, 55(12): 122801

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