[1] N DOBIGEON, J Y TOURNERET, C RICHARD et al. Nonlinear unmixing of hyperspectral images: Models and algorithms. IEEE Signal Processing Magazine, 31, 82-94(2013).
[2] Xiaoming WU. The research on hyperspectral imagery unmixing technology based on kernel methods(2011).
[3] J M BIOUCAS-DIAS, A PLAZA, G CAMPS-VALLS et al. Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and remote sensing magazine, 1, 6-36(2013).
[4] Wenyu WANG, Guoyin CAI. Endmember extraction by pure pixel index algorithm from hyperspectral image, 7157, 71570E(2009).
[5] J M NASCIMENTO, J M DIAS. Vertex component analysis: A fast algorithm to unmix hyperspectral data. IEEE transactions on Geoscience and Remote Sensing, 43, 898-910(2005).
[6] M E WINTER. N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data. Proceedings of Spie the International Society for Optical Engineering, 3753, 266-275(1999).
[7] Ersen LI, Shulong ZHU, Xiaoming ZHOU等. The development and comparison of endmember extraction algorithms using hyperspectral imagery. Journal of Remote Sensing, 15, 659-679(2011).
[8] Lidan MIAO, Hairong QI. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization. IEEE Transactions on Geoscience and Remote Sensing, 45, 765-777(2007).
[9] D SHAH, T ZAVERI, Y N TRIVEDI et al. Entropy-based convex set optimization for spatial-spectral endmember extraction from hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 4200-4213(2020).
[10] Ying WANG, Nan LIANG, Lei GUO. A hyperspectral remote sensing image endmember extraction algorithm based on modified extended-morphological oper. Acta Photonica Sinica, 41, 672-677(2012).
[11] D C HEINZ. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 39, 529-545(2001).
[12] Xiping JIANG, Yu JIANG, Fang WU et al. Quantitative interpretation of mineral hyperspectral images based on principal component analysis and independent component analysis methods. Applied Spectroscopy, 68, 502-509(2014).
[13] M ARNGREN, M N SCHMIDT, J LARSEN. Unmixing of hyperspectral images using Bayesian non-negative matrix factorization with volume prior. Journal of Signal Processing Systems, 65, 479-496(2011).
[14] J M NASCIMENTO, J M DIAS. Does independent component analysis play a role in unmixing hyperspectral data?. IEEE Transactions on Geoscience and Remote Sensing, 43, 175-187(2005).
[15] D LEE, H S SEUNG. Algorithms for non-negative matrix factorization. Advances in Neural Information Processing Systems, 13, 556-562(2000).
[16] Zuyuan YANG, Guoxu ZHOU, Shengli XIE et al. Blind spectral unmixing based on sparse nonnegative matrix factorization. IEEE Transactions on Image Processing, 20, 1112-1125(2010).
[17] S A VAVASIS. On the complexity of nonnegative matrix factorization. SIAM Journal on Optimization, 20, 1364-1377(2010).
[18] Wei HE, Hongyan ZHANG, Liangpei ZHANG. Total variation regularized reweighted sparse nonnegative matrix factorization for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 55, 3909-3921(2017).
[19] Sen JIA, Yuntao QIAN, Zhen JI. Nonnegative matrix factorization with piecewise smoothness constraint for hyperspectral unmixing(2008).
[20] Yuntao QIAN, Sen JIA, Jun ZHOU et al. Hyperspectral unmixing via L_1/2 sparsity-constrained nonnegative matrix factorization. IEEE Transactions on Geoscience and Remote Sensing, 49, 4282-4297(2011).
[21] Risheng HUANG. Research on unmixing methods based on nonnegative matrix factorization for hyperspectral remote sensing(2020).
[22] Yanfeng GU, Ye ZHANG, Junping ZHANG. Integration of spatial-spectral information for resolution enhancement in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 46, 1347-1358(2008).
[23] A ZARE, P GADER. Hyperspectral band selection and endmember detection using sparsity promoting priors. IEEE Geoscience and Remote Sensing Letters, 5, 256-260(2008).
[24] Jianqing FAN, Runze LI. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96, 1348-1360(2001).
[25] Jianqing FAN, Heng PENG. Nonconcave penalized likelihood with a diverging number of parameters. The Annals of Statistics, 32, 928-961(2004).
[26] E J CANDES, M B WAKIN, S P BOYD. Enhancing sparsity by reweighted ℓ1 minimization. Journal of Fourier Analysis and Applications, 14, 877-905(2008).
[27] L I RUDIN, S OSHER, E FATEMI. Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena, 60, 259-268(1992).
[28] S Z LI. Markov random field modeling in image analysis, 129-159(2009).
[29] A BECK, M TEBOULLE. Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Transactions on Image Processing, 18, 2419-2434(2009).