[1] ZHANG B. The advances of hyperspectral image processing and information extraction[J]. Journal of Remote Sensing, 2016, 20(5): 1061-1090(in Chinese).
[2] ZHANG B, GAO L R. Hyperspectral image classification and target detection[M]. 3rd ed. Beijing:Science Press, 2011: 1-15 (in Ch-inese).
[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 A-pplied Earth Observations & Remote Sensing, 2012, 5(2): 354-379.
[4] TANG X Y, GAO K, NI G Q. Advances in nonlinear spectral unmixing of hyperspectral images[J]. Remote Sensing Technology and Application, 2013, 28(4): 731-738(in Chinese).
[5] YANG B, WANG B. Review of nonlinear unmixing for hyperspectral remote sensing imagery[J]. Journal of Infrared and Millimeter Waves, 2017, 36(2): 173-185(in Chinese).
[6] FENG W Y, CHEN Q, HE W J, et al. A defogging method based on hyperspectral unmixing[J]. Acta Optica Sinica, 2015, 35(1): 110002 (in Chinese). doi: 10.3788/AOS201535.0110002
[7] XU X, SHI Zh W. Multi-objective based spectral unmixing for hyperspectral images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 124: 54-69. doi: 10.1016/j.isprsjprs.2016.12.010
[8] CUI J T. The study on hyperspectral image endmember unmixing[D]. Xi'an: Xidian University, 2013: 1-4 (in Chinese).
[9] WU X M. The research on hyperspectral imagery unmixing technology based on kernel methods[D].Hangzhou: Zhejiang University, 2011: 5-6 (in Chinese).
[10] YUAN J, ZHANG Y J, GAO F P. An overview on linear hyperspectral unmixing [J]. Journal of Infrared and Millimeter Waves, 2018, 37(5): 553-571 (in Chinese).
[11] HEINZ D C, CHANG C I. Fully constrained least squares li-near spectral mixture analysis method for material quantification in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3): 529-545. doi: 10.1109/36.911111
[12] GAO T. Research status and future development of hyperspectral unmixing algorithm based on nonnegative matrix decomposition [J]. Science & Technology Vision, 2015(13): 172 (in Chinese).
[13] LUO W F, ZHONG L, ZHANG B, et al. An independent component analysis technique for spectral decomposition of hyperspectral remote sensing images [J]. Spectroscopy and Spectral Analysis, 2010, 30(6): 1628-1633(in Chinese).
[14] GUILFOYLE K J, ALTHOUSE M L, CHANG C L. A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks[J]. IEEE Transactions on Geoscience & Remote Sensing, 2001, 39(10): 2314-2318.
[15] LI X R WU X M, ZHAO L Y. Unsupervised nonlinear decomposition of hyperspectral mixed pixels [J]. Journal of Zhejiang University(Engineering Science Edition), 2011, 45(4): 607-613(in Ch-inese).
[16] XIA W, LIU X, WANG B, et al. Independent component analysis for blind unmixing of hyperspectral imagery with additional constraints[J]. IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(6): 2165-2179.
[17] WU K, ZHANG L P, LI P X. A neural network method of selective endmember for pixel unmixing[J]. Journal of Remote Sensing, 2007, 11(1): 20-26(in Chinese).
[18] LIU W J, YANG X H, QU H Ch. Hyperspectral unmixing algorithm based on Lagrangian [J]. Application Research of Computers, 2016, 33(10): 3173-3176(in Chinese).
[19] LIU Z G, LU Y L, WEI Y W. Supervised method for hyperspectral image camouflage target detection[J]. Infrared and Laser Engineering, 2013, 42(11): 3076-3081(in Chinese).