[1] Chang C I. Hyperspectral imaging: techniques for spectral detection and classification[M]. New York: Plenum, 2003.
[2] Winter M E. N-findr: an algorithm for fast autonomous spectral endmember determination in hyperspectral data[J]. Proc. of the SPIE imaging spectrometry V,1999,3753: 266-275.
[3] Nascimento J, Bioucas-Dias J. Vertex component analysis: a fast algorithm to unmix hyperspectral data[J]. IEEE Trans. Geosci. Remote Sens.,2002,43(4): 898-910.
[4] Chang C I, Wu C C, Liu W, et al. A new growing method for simplex-based endmember extraction algorithm[J]. IEEE Trans. Geosci. Remote Sens.,2006,44(10): 2804-2819.
[5] Heinz D C, Chang C I. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J]. IEEE Trans. Geosci. Remote Sens.,2001,39(3): 529-545.
[6] Hyvarinen A, Karhunen J, Oja E, Independent Component Analysis[M]. New York: Wiley, 2001.
[7] Chang C I, Wang J. Applications of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery[J]. IEEE Trans. Geosci. Remote Sens.,2006,44(9): 2601-2616.
[8] Lee T, Girolami M, Sejnowski T. Independent component analysis using an extended infomax algorithm for mixed subgaussian and supergaussian sources[J]. Neural Computation,1999,11(2): 417-441.
[9] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature,1999,401: 788-791.
[10] Miao L, Qi H. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization[J]. IEEE Trans. Geosci. Remote Sens.,2007,45(3): 765-777.
[11] Landgrebe D. Multispectral data analysis: A signal theory perspective[R]. West Lafayette: School of Electrical & Computer Engineering, Purdue University,1998.