[1] STEIN D W J, BEAVEN S G, HOFF L E, et al. Anomaly detection from hyperspectral imagery［J］. Signal Processing Magazine, IEEE, 2002, 19(1): 58-69.

[2] MANOLAKIS D, SHAW G. Detection algorithms for hyperspectral imaging applications［J］. Signal Processing Magazine, IEEE, 2002, 19(1): 29-43.

[3] DU Bo, ZHANG Liangpei. Randomselectionbased anomaly detector for hyperspectral imagery［J］. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(5): 1578-1589.

[4] 杜博，张乐飞，张良培，等. 高光谱图像降维的判别流形学习方法［J］. 光子学报，2013, 42(3): 320-325.

    DU Bo, ZHANG Lefei, ZHANG Liangpei, et al. Discriminant manifold learning approach for hyperspectral image dimension reduction［J］. Atca Photonica Sinica, 2013, 42(3): 320-325.

[5] MATTEOLI S, DIANI M, CORSINI G. A tutorial overview of anomaly detection in hyperspectral images［J］. Aerospace and Electronic Systems Magazine, IEEE, 2010, 25(7): 5-28.

[6] 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, 1990, 38(10): 1760-1770.

[7] 赵春晖，胡春梅，包玉刚. 一种背景误差累积的高光谱图像异常检测算法［J］. 光子学报，2010, 39(10): 1830-1835.

    ZHAO Chunhui, HU Chunmei, BAO Yugang. A kind of anomaly detection algorithm for hyperspectral image through background error cumulation［J］. Atca Photonica Sinica, 2010, 39(10): 1830-1835.

[8] 蒲晓丰，雷武虎，黄涛，等. 基于稳健背景子空间的高光谱图像异常检测［J］. 光子学报，2010, 39(12): 2224-2228.

    PU Xiaofeng, LEI Wuhu, HUANG Tao, et al. Robust background subspace based anomaly detection algorithm for hyperspectral imagery［J］. Atca Photonica Sinica, 2010, 39(12): 2224-2228.

[9] BILLOR N, HADI A S, VELLEMAN P F. BACON: blocked adaptive computationally efficient outlier nominators［J］. Computational Statistics & Data Analysis, 2000, 34(3): 279-298.

[10] CARLOTTO M J. A clusterbased approach for detecting manmade objects and changes in imagery［J］. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(2): 374-387.

[11] BANERJEE A, BURLINA P, DIEHL C. A support vector method for anomaly detection in hyperspectral imagery［J］. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(8): 2282-2291.

[12] 王挺，杜博，张良培. 顾及局域信息的核化正交子空间投影目标探测方法［J］. 武汉大学学报信息科学版，2013, 38(2): 200-203.

    WANG Ting, DU Bo, ZHANG Liangpei. Kernel orthogonal subspace projection for target detection give consideration to local information［J］. Geomatics and Information Science of Wuhan University, 2013, 38(2): 200-203.

[13] HEESUNG KWON, NASRABADI N M. Kernel RXalgorithm: a nonlinear anomaly detector for hyperspectral imagery［J］. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(2): 388-397.

[14] HEESUNG KWON, NASRABADI N M. Kernel orthogonal subspace projection for hyperspectral signal classification［J］. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(12): 2952-2962.

[15] HEESUNG KWON, NASRABADI N M. Kernel matched signal detectors for hyperspectral target detection［C］. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.

[16] HEESUNG KWON, NASRABADI N M. Kernel matched subspace detectors for hyperspectral target detection［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(2): 178-194.

[17] GURRAM P, HEESUNG KWON, HAN T. Sparse kernelbased hyperspectral anomaly detection［J］. Geoscience and Remote Sensing Letters, IEEE, 2012, 9(5): 943-947.

[18] 梅锋，赵春晖，孙岩，等. 基于新型光谱相似度量核的高光谱异常检测算法［J］. 光子学报，2009, 38(12): 3165-3170.

    MEI Feng, ZHAO Chunhui, SUN Yan, et al. A novel spectral similarity measurement kernel based anomaly detection method in hyperspectral imagery［J］. Atca Photonica Sinica, 2009, 38(12): 3165-3170.

[19] EDISANTER L, INGRAM J. Hyperspectral anomaly detection based on minimum generalized variance method［C］. SPIE, 2008, 6966: 1-7.

[20] HIRSH R G. A performance charcterization of kernelbased algorithms for anomaly detection in hyperspectral imagery［D］. Maryland: University of Maryland, 2007.

[21] GU Yanfeng, LIU Ying, ZHANG Ye. A selective KPCA algorithm based on highorder statistics for anomaly detection in hyperspectral imagery［J］. Geoscience and Remote Sensing Letters, IEEE, 2008, 5(1): 43-47.