[1] [1] TONG Qing-xi, ZHANG Bing, ZHENG Lan-fen. Hyperspectral remote sensing: principle, technology and application[M]. Beijing: Higher Education Press, 2006.

[2] [2] JIMENEZ L O, LANDGREBE D A. Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 1998, 28(1): 39-54.

[3] [3] NASCIMENTO J M P, BIOUCAS-DIAS J M.Hyperspectral unmixing based on mixtures of Dirichlet components[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(3): 863-878.

[4] [4] KESHAVA N. A survey of spectralunmixing algorithms[J]. Lincoln Laboratory Journal, 2003: 55-78.

[5] [5] PLAZA A, MARTN G, PLAZA J,et al. Recent developments in endmember extraction and spectral unmixing[M]. Optical Remote Sensing. 2011: 235-267.

[6] [6] BOARDMAN J W, KRUSE F A, GREEN R O. Mapping target signatures via partialunmixing of AVIRIS data[C]. Summaries 5th JPL Airborne Earth Science Workshop 1995.1: 23-26.

[7] [7] CHANG C I, PLAZA A. A fast iterative algorithm forimplementation of pixel purity index[J]. IEEE Geoscience and Remote Sensing Letters, 2006, 3(1): 63-67.

[8] [8] WINTER M E. A proof of the N-FINDR algorithm for the automated detection of endmembers in a hyperspectral image[C].Defense and Security. International Society for Optics and Photonics, 2004: 31-41.

[9] [9] ZHAO Chun-hui, GUO Yun-ting. An improved fast N-FINDR endmember extraction algorithm[J]. Acta Photonica Sinica, 2015, 44(10): 1022001

[10] [10] CRAIG M D.Minimum volume transforms for remotely sensed data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(3): 542-552.

[11] [11] NASCIMENTO J M P, DIAS J M B. Vertex component analysis: a fast algorithm tounmix hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 898-910.

[12] [12] LIU Wei-xiang, ZHENG Nan-ning, YOU Qu-bo. Nonnegative matrix factorization and its applications in pattern recognition[J]. Chinese Science Bulletin, 2006, 51(1): 7-18.

[13] [13] MIAO Li-dan, QI Hai-rong. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(3): 765-777.

[14] [14] LIU Jian-jun, WU Ze-bin, WEI Zhi-hui, et al. A fast algorithm for hyperspectral unmixing based on constrained nonnegative matrix factorization[J]. Acta Electronica Sinica, 2013, 41(3): 432-437.

[15] [15] GENG Xiu-rui, JI Lu-yan, ZHAO Yong-chao, et al. A new endmember generation algorithm based on a geometric optimization model for hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4): 811-815.

[16] [16] JI Lu-yan, GENG Xiu-rui, YU Kai, et al. A new non-negative matrix factorization method based on barycentric coordinates for endmember extraction in hyperspectral remote sensing[J]. International Journal of Remote Sensing, 2013, 34(19): 6577-6586.

[17] [17] YANG Bin, LUO Wen-fei. Constrained NMF-based high-dimension adaptive particle swarm optimization algorithm for endmember extraction from a hyper spectral remote sensing image[J]. Journal of Remote Sensing, 2015, 19(2): 240-253.

[18] [18] SAJDA P,DU S. Recovery of constituent spectra using non-negative matrix factorization[C]. SPIE, 2003, 5207: 321-331.

[19] [19] LEE D D. Algorithms for nonnegative matrix factorization[J]. Advances in Neural Information Processing Systems, 2000, 13(6): 556-562.

[20] [20] GENG Xiu-rui, ZHAO Yong-chao, WANG Fu-xiang, et al. A new volume formula for a simplex and its application to endmember extraction for hyperspectral image analysis[J]. International Journal of Remote Sensing, 2010, 31(4): 1027-1035.

[21] [21] CHANG C I, DU Q. Estimation of number of spectrally distinct signal sources in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3): 608-619.

[22] [22] HEINZ D C, CHANG C. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 39(3): 529-545.

[23] [23] CHANG C, HEINZ D C. Constrainedsubpixel target detection for remotely sensed imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(3): 1144-1159.

[24] [24] PLAZA A, MARTINEZ P, PEREZ R,et al. A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3): 650-663.