一种基于核特征空间的鲁棒性高光谱异常探测方法
[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 Liangpei. Randomselectionbased 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.
[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 multipleband 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.
[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.
[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 clusterbased approach for detecting manmade 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 Liangpei. 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 RXalgorithm: 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 kernelbased 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.
[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 kernelbased algorithms for anomaly detection in hyperspectral imagery[D]. Maryland: University of Maryland, 2007.
[21] GU Yanfeng, LIU Ying, ZHANG Ye. A selective KPCA algorithm based on highorder statistics for anomaly detection in hyperspectral imagery[J]. Geoscience and Remote Sensing Letters, IEEE, 2008, 5(1): 43-47.
赵锐, 杜博, 张良培. 一种基于核特征空间的鲁棒性高光谱异常探测方法[J]. 光子学报, 2013, 42(8): 883. ZHAO Ruia, DU Bob, ZHANG Liangpeia. An Anomaly Detection Method for Hyperspectral Imagery in Kernel Feature Space Based on Robust Analysis[J]. ACTA PHOTONICA SINICA, 2013, 42(8): 883.