[1] [1] DU B, ZHANG L. A discriminative metric learning based anomaly detection method［J］. IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(11): 6844-6857.

[2] [2] DU B, ZHANG L. Target detection based on a dynamic subspace［J］. Pattern Recognition, 2014, 47(1):344-358.

[3] [3] SCHAUM A. Spectral subspace matched filtering［C］. Proceedings of the SPIE-the International Society for Optical Engineering, 2001, 4381: 1 17

[4] [4] HARSANYI J C, CHANG C I. Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach［J］. IEEE Transaction on Geoscience and Remote Sensing, 1994, 32(4): 779-785.

[5] [5] DU B, ZHANG Y, ZHANG L, et al. A hypothesis independent subpixel target detector for hyperspectral images［J］. Signal Processing, 2015, 110: 244-249.

[6] [6] REED I S，YU X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution［J］. IEEE Tran Sactions on Acoustics Speech and Signal Processing, 1990, 38(10)：1760-1770.

[7] [7] HEESUNG K, NASRABADI N M. Kernel RX-algorithm：anonlinear anomaly detector for hyperspectral imagery［J］. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(2)：388-397.

[8] [8] MEI Feng, ZHAO Chun-hui, SUN Yan, et al. A novel spectral similarity measurement kernel based anomaly detection method in hyperspectral imagery［J］. Acta Photonica Sinica, 2009, 18(5)：3165-3170.

[9] [9] ZHANG Xiu-bao, YUAN Yan, JING Juan-juan, et al. Spectral discrimination method information divergence combined with gradient angle［J］. Spetroscopy and Spectral Analysis, 2011, 31（3）：853-857.

[10] [10] WANG Liang-liang, LI Zhi-yong, SUN Ji-xiang. Anomaly detection in hyperspectral imagery based on spectral gradient and LLE［J］. Applied Mechanics and Materials, 2012, 121（10） ： 720-724

[11] [11] WANG Liang-liang, LI Zhi-yong, SUN Ji-xiang. Improved RX algorithm with global statistics［J］. Applied Mechanics and Materials, 2014, 446（11） ：942-945

[12] [12] MEI Feng, ZHAO Chun-hui. Spatial filter based anomaly detection algorithm for hyperspectral imagery kernel RX detectors［J］. Journal of Harbin Engineering University, 2009, 30(6)：697-702.

[13] [13] SCHOLKOPF B, SMOLA A J, MULLER K. Nonlinear component analysis a Kernel eigenvalue problem ［J］. Neural Computation, 1998, 10(5)：1299-1319.

[14] [14] ZHAO Chun-hui, LI Jie, MEI Feng. A kernel weighted RX algorithm for anomaly detection in hyperspectral imagery［J］. Iournal of Infrared and Millimeter Waves, 2010, 29(5)：378-382.