[3] [3] Keshava N, Mustard J F. Spectral unmixing$$J$$. IEEE Signal Processing Magazine, 2002, 19(1): 44-57.

[4] [4] Chang C I, Plaza A. A fast iterative algorithm for implementation of pixel purity index$$J$$. IEEE Geoscience and Remote Sensing Lett., 2006, 3(1): 63-67.

[5] [5] Ren H, Chang C I. Automatic spectral target recognition in hyperspectral imagery$$J$$. IEEE Trans. on Aerospace and Electronic Systems, 2003, 39(4): 1232-1249.

[6] [6] Nascimento J M P, Dias J M B. Vertex component analysis: a fast algorithm to unmix hyperspectral data$$J$$. IEEE Trans. on Geoscience and Remote Sensing, 2005, 43(4): 898-910.

[7] [7] Winter M E. N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data$$J$$. Proc. of SPIE., 1999, 3753: 266-275.

[8] [8] Tenenbaum J B, Silva V, Langford J C. A global geometric framework for nonlinear dimensionality reduction$$J$$. Science, 2000, 290(5500): 2319-2323.

[9] [9] Achanta R, Shaji A, Smith K, et al. SLIC superpixels compared to state-of-the-art superpixel methods$$J$$. IEEE Trans. on Pattern Analysis & Machine Intelligence, 2012, 34(11): 2274-2282.

[10] [10] Torgerson W S. Multidimensional scaling: Ⅰ. Theory and method$$J$$. Psychometrika, 1952, 17(4): 401-419.

[13] [13] Chang Y C C, Ren H, Chang C I, et al. How to design synthetic images to validate and evaluate hyperspectral imaging algorithms$$J$$. Proc. of SPIE., 2008, 6966: 489-499.

[14] [14] Chang C I, Du Q. Estimation of number of spectrally distinct signal sources in hyperspectral imagery$$J$$. IEEE Trans. on Geoscience & Remote Sensing, 2004, 42(3): 608-619.