[1] [1] Zhang L P, Zhang L F. Hyperspectral remote sensing［M］.Wuhan: Wuhan University Press, 2005: 23-24.

[2] [2] 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.

[3] [3] Bioucas-Dias J M, Plaza A, Dobigeon N, et al. Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches［J］. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(2): 354-379.

[4] [4] Winter M E. N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data［J］. Proceedings of SPIE, 1999, 3753: 266-275.

[5] [5] Nascimento J M P, Dias J M B. Vertex component analysis: a fast algorithm to unmix hyperspectral data［J］. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 898-910.

[6] [6] Wang L J, Li X R, Zhao L Y. Fast implement of the simplex growing algorithm for endmember extraction［J］. Acta Optica Sinica, 2014, 34(11): 1128001.

[7] [7] Boardman J W, Kruse F A, Green R O. Mapping target signatures via partial unmixing of AVIRIS data［C］. Fifth JPL Airborne Earth Science Workshop, 1995: 23-26.

[8] [8] Ambikapathi A M, Chan T H, Ma W K, et al. A robust alternating volume maximization algorithm for endmember extraction in hyperspectral images［C］. Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010: 1-4.

[9] [9] Iordache M D, Bioucas-Dias J M, Plaza A. Total variation spatial regularization for sparse hyperspectral unmixing［J］. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(11): 4484-4502.

[10] [10] Li S Y, Du S S, Zeng Z Y. Decoy spectrum design based on feature space significance［J］. Acta Optica Sinica, 2017, 37(1): 0128001.

[11] [11] Geng X, Zhao Y C, Wang F X, 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.

[12] [12] 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.

[13] [13] Hartigan J A, Wong M A. AlgorithmAS 136: A K-means clustering algorithm［J］. Applied Statistics, 1979, 28(1): 100-108.

[14] [14] Martin G, Plaza A. Region-based spatial preprocessing for endmember extraction and spectral unmixing［J］. IEEE Geoscience and Remote Sensing Letters, 2011, 8(4): 745-749.

[15] [15] Miao L D, Qi H R. 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.

[16] [16] Heinz D C, Chang C I. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery［J］. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3): 529-545.