[1] [1] REED I S, YU X. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution ［J］. IEEE Trans on Acoustics, Speech, and Signal Processing, 1990, 38(10): 1760-1770.

[2] [2] LO E, INGRAM J. Hyperspectral anomaly detection based on minimum generalized variance method ［C］. SPIE, 2008, 6966: 696603-1-7.

[3] [3] MATTEOLI S, DIANI M, CORSINI G. Improved covariance matrix estimation: interpretation and experimental analysis of different approaches for anomaly detection application ［C］. SPIE, 2009, 7477: 74770K-1-9.

[4] [4] HSUEH M, CHANG C I. Adaptive causal anomaly detection for hyperspectral imagery ［C］. 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS ′04), 2004:3222-3224.

[5] [5] TAKESHITA T, NOZAWA S, KIMURA F. On the bias of mahalanobis distance due to limited sample size effect ［C］. IEEE, Proceedings of Second International Conference Document Analysis and Recognition, 1993: 171-174.

[6] [6] LI Guo-hong, SHI Peng-fei. Modified mahalanobis distance by compensation for error of non-dominant eigenvalues and asymmetrical distribution ［J］. Acta Electronica Sinica, 2007, 35(4): 747-750.

[7] [7] GAO Yong-hong. Data depth based on spatial rank ［J］. Statistics & Probability Letters, 2003, 65(3): 17-225.