[1] [1] CHEN J. On Classification Method of Hyperspectral Images ［D］. Changsha: National University of Defense Technology,2010. (in Chinese)

[2] [2] MELGANI F,BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines ［J］. IEEE Trans. Geosci. Remote Sens., 2004,42(8):1778-1790.

[3] [3] CAMPS-VALLS G, BRUZZONE L. Kernel-based methods for hyperspectral image classification ［J］. IEEE Trans. Geosci. Remote Sens., 2005,43(6):1351-1362.

[4] [4] MATHIEU F, BENEDIKTSSON J A, CHANUSSOT J, et al.. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles ［J］. IEEE Trans. Geosci. Remote Sens., 2008,46(11):3804-3814.

[5] [5] TUIA D, PACIFICI F, KANEVSKI M, et al.. Classification of very high spatial resolution imagery using mathematical morphology and support vector machines ［J］, IEEE Trans. Geosci. Remote Sens., 2009,47(11):3866-3879.

[6] [6] FAUVEL M. Spectral and spatial methods for the classification of urban remote sensing data ［D］. Reykjavik:University of Iceland, 2007.

[7] [7] TARABALKA Y, BENEDIKTSSON J A, CHANUSSOT J. Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques ［J］. IEEE Trans. Geosci. Remote Sens., 2009,47(8):2973-2987.

[8] [8] NIAN Y J, WANG ZH, WAN J W, et al.. Compression technique for hyperspectral imagery oriented anomaly detection［J］. Journal of National University of Defense Technology,2009,31(3):48-52. (in Chinese)

[9] [9] SU L H, LI G, YI T SH, et al.. A robust hyperspectral image compression method ［J］. Opt. Precision Eng., 2007,15(10):1609-1615. (in Chinese)

[10] [10] CHANG C I, DU Q. Estimation of number of spectrally distinct signal sources in hyperspectral imagery ［J］. IEEE Trans. Geosci. Remote Sens., 2004,42(3):608-619.

[11] [11] ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/92 AV3C/［OL］.

[12] [12] GONZALEZ R C, WOODS R E. Digital Image Processing ［M］．USA：Prentice Hall, 2008.