[1] ZHANG L P, ZHANG L F. Hyperspectral remote sensing[M].Wuhan:Wuhan University Press, 2005:26-27(in Chinese).
[2] XIA W. Researches on the methods of unmixing and band selection for hyperspectral remote sensing images[D]. Shanghai: Fudan University, 2013: 15-16(in Chinese).
[3] NASH D B, CONEL J E. Spectral reflectance systematics for mixtures of powdered hypersthene, labradorite, and ilmenite[J].Journal of Geophysical Research, 1974, 79(11):1615-1621. doi: 10.1029/JB079i011p01615
[4] BRUCE H. Book review:Theory of reflectance and emittance spectroscopy[M].Cambridge:Cambridge University Press, 1993:455-460.
[5] BOARDMAN J W, KRUSE F A, GREEN R O.Mapping target signatures via partial unmixing of AVIRIS data in summaries[C]//Summaries, Fifth JPL Airborne Earth Science Workshop. Pasadena, USA: JPL Publication, 1995: 23-262.
[6] WINTER M E. N-FINDR:an algorithm for fast autonomous spectral endmember determination in hyperspectral data[J].Proceedings of the SPIE, 1999, 3753:266-275. doi: 10.1117/12.366289
[7] NASCIMENTO J M P, DIAS J M B.Vertex component analysis:a fast algorithm to unmix hyperspectral data[J].IEEE Transactions on Geosciences and Remote Sensing, 2005, 43(4):898-910. doi: 10.1109/TGRS.2005.844293
[8] ZHUO L, CAO J J, WANG F, et al. Blind unmixing based on improved target endmember for hyperspectral imagery[J]. Journal of Remote Sensing, 2015, 19(2):273-287(in Chinese).
[9] HARSANYI J C, CHANG C I. Hyperspectral image classification and dimensionality reduction:an orthogonal subspace projection approach[J].IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(4):779-785. doi: 10.1109/36.298007
[10] GRUNINGER J, RATKOWSKI A J, HOKE M L. The sequential maximum angle convex cone endmember model[J].Proceedings of the SPIE, 2004, 5425:1-14. doi: 10.1117/12.543794
[11] JIA S, QIAN Y T. Constrained nonnegative matrix factorization for hyperspectral unmixing[J].IEEE Transaction on Geoscience and Remote Sensing, 2009, 47(1):161-173. doi: 10.1109/TGRS.2008.2002882
[12] HYVARINEN A, KARHUNEN J, OJA E. Independent component analysis[M].New York, USA:John Wiley & Sons Ltd, 2001:89-96.
[13] ZHAO Ch H, CUI Sh L, ZHAO G P, et al. Endmember extraction algorithm based on improved iterative error analysis[J]. Journal of Harbin Engineering University, 2015, 36(2):257-261(in Chinese).
[14] PLAZA A, MARTINEZ P, PEREZ R, et al. Spatial/spectral endmember extraction by multidimensional morphological operations[J].IEEE Transactions on Geosciences and Remote Sensing, 2002, 40(9):2025-2040. doi: 10.1109/TGRS.2002.802494
[15] SHEN W X. Simplex theory guidance:high dimensional generalization of triangles[M].Changsha:Hunan Normal University Press, 2000:35(in Chinese).
[16] ANTONIO P, PABLO M, ROSA P, et al. A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data[J].IEEE Transaction on Geoscience and Remote Sensing, 2004, 42(3):650-663. doi: 10.1109/TGRS.2003.820314
[17] WANG K, QU H M. Anomaly detection method based on improved minimum noise fraction transformation[J].Laser Technology, 2015, 39(3):381-385(in Chinese).
[18] ZENG F X. The improvement and optimization of endmembers extraction in hyperspectral remote sensing image[D].Chengdu: Chengdu University of Technology, 2013: 19-20(in Chinese).
[19] GENG X, ZHAO Y, WANG F, 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. doi: 10.1080/01431160903154283
[20] CHARLES L, LAWSON, RICHARD J. Solving least squares pro-blem[M].New Jersey, USA:Prentice-Hall, 1995:64-80.
[21] HEINZ D C, CHANG C I. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J].IEEE Transaction on Geoscience and Remote Sensing, 2001, 39(3):529-545. doi: 10.1109/36.911111
[22] MEI Sh H, HE M Y. A novel spectrum filter for fully constrained mixture analysis[J]. Journal of Remote Sensing, 2010, 14(1):68-79.
[23] LEE D D, SEUNG H S. Learning the parts of objects by nonnegative matrix factorization[J].Nature, 1999, 401(6755):788-791. doi: 10.1038/44565
[24] MIAO L, QI H. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix fraction[J].IEEE Transaction on Geoscience and Remote Sensing, 2007, 45(3):765-777. doi: 10.1109/TGRS.2006.888466
[25] QIAN Y T, JIA S. Hyperspectral unmixing via L1/2 sparsity-constrained nonnegative matrix factorization[J].IEEE Transaction on Geoscience and Remote Sensing, 2011, 49(11):4282-4297. doi: 10.1109/TGRS.2011.2144605
[26] YU Y, GUO Sh, SUN W D. Minimum distance constrained nonnegative matrix factorization for the end-member extraction of hyperspectral images[J]. High Technology Letters, 2012, 18(4):333-342.
[27] BAYLISS J D, GUALTIERI J A, CROMP R F. Analyzing hyperspectral data with independent component analysis[J].Proceedings of the SPIE, 1998, 3240:133-143. doi: 10.1117/12.300050
[28] CHEN C H, ZHANG X. Independent component analysis for remote sensing study[J].Proceedings of the SPIE, 1999, 3871:150-158. doi: 10.1117/12.373252
[29] CHIANG S S, CHANG C I, GINSBERG I W. Unsupervised hyperspectral image analysis using independent component analysis[C]//Proceedings, Geoscience and Remote Sensing Symposium. Honolulu, HI, USA: IEEE International Geoscience & Remote Sensing, 2000: 3136-3138.
[30] ZORTEA M, PLAZA A. Spatial preprocessing for endmember extraction[J]. IEEE Transaction on Geoscience and Remote Sensing, 2009, 47(8):2679-2693. doi: 10.1109/TGRS.2009.2014945
[31] MARTIN G, PLAZA A. Region-based spatial preprocessing for endmember extraction and spectral unmixing[J].IEEE Transaction on Geoscience and Remote Sensing, 2011, 8(4):745-749. doi: 10.1109/LGRS.2011.2107877
[32] MARTIN G, PLAZA A. Spatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data[J].IEEE Transaction on Geoscience and Remote Sensing, 2012, 5(2):380-395.