• Journal of Infrared and Millimeter Waves
  • Vol. 37, Issue 5, 553 (2018)
YUAN Jing1、*, ZHANG Yu-Jin1, and GAO Fang-Ping2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.11972/j.issn.1001-9014.2018.05.008 Cite this Article
    YUAN Jing, ZHANG Yu-Jin, GAO Fang-Ping. An overview on linear hyperspectral unmixing[J]. Journal of Infrared and Millimeter Waves, 2018, 37(5): 553 Copy Citation Text show less
    References

    [2] 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.

    [3] Zhang B, Zhuang L, Gao L, et al.PSO-EM: A Hyperspectral Unmixing Algorithm Based On Normal Compositional Model[J].IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(12):7782-7792.

    [4] Lu G, Qin X, Wang D, et al.Estimation of tissue optical parameters with hyperspectral imaging and spectral unmixing(M).2015.

    [5] Matsuki T, Yokoya N, Iwasaki A.Hyperspectral Tree Species Classification of Japanese Complex Mixed Forest With the Aid of Lidar Data[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(5):2177-2187.

    [6] Brook A. Quantitative detection of settled dust over green canopy[C]// The Workshop on Hyperspectral Image & Signal Processing: Evolution in Remote Sensing. EGU General Assembly Conference Abstracts, 2017:1-4.

    [7] Alam M S, Sidike P. Trends in oil spill detection via hyperspectral imaging[C]// International Conference on Electrical & Computer Engineering. IEEE, 2013:858-862.

    [8] Lin H, Zhang X.Retrieving the hydrous minerals on Mars by sparse unmixing and the Hapke model using MRO/CRISM data[J].Icarus, 2017, 288:160-171.

    [9] Baskurt D O, Omruuzun F, Cetin Y Y. Hyperspectral unmixing based analysis of forested areas[C]// Signal Processing and Communications Applications Conference. IEEE, 2015:2329-2332.

    [10] Das B S, Sarathjith M C, Santra P, et al.Hyperspectral remote sensing: opportunities, status and challenges for rapid soil assessment in India[J].Current Science, 2015, 108(5):860-868.

    [13] Adams J B, Gillespie A R. Remote sensing of landscapes with spectral images: a physical modeling approach[M]. Cambridge University Press, 2006.

    [14] Heylen R, Parente M, Gader P.A review of nonlinear hyperspectral unmixing methods[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 1844-1868.

    [15] Shi C, Wang L.Incorporating spatial information in spectral unmixing: A review[J].Remote Sensing of Environment, 2014, 149: 70-87.

    [16] Wang L, Shi C, Diao C, et al.A survey of methods incorporating spatial information in image classification and spectral unmixing[J].International Journal of Remote Sensing, 2016, 37(16): 3870-3910.

    [19] Winter M E.N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data[C]//SPIE's International Symposium on Optical Science, Engineering, and Instrumentation,International Society for Optics and Photonics, 1999:266-275.

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

    [21] Rajabi R, Ghassemian H.Spectral Unmixing of Hyperspectral Imagery Using Multilayer NMF[J].IEEE Geoscience & Remote Sensing Letters, 2014, 12(1):38-42.

    [22] Mei S, Bi Q, Ji J, et al. Spectral Variation Alleviation by Low-Rank Matrix Approximation for Hyperspectral Image Analysis[J]. IEEE Geoscience & Remote Sensing Letters, 2016, 13(6):796-800.

    [23] Ghaffari O, Zoej M J V, Mokhtarzade M, et al. Reducing the Effect of the Endmembers′ Spectral Variability by Selecting the Optimal Spectral Bands[J]. Remote Sensing, 2017, 9(9):884.

    [24] Somers B, Delalieux S, Verstraeten W W, et al. An automated waveband selection technique for optimized hyperspectral mixture analysis[J]. International Journal of Remote Sensing, 2010, 31(20):5549-5568.

    [25] Donoho D L, Stodden V C.When Does Non-Negative Matrix Factorization Give a Correct Decomposition into Parts [C]//Neural information processing systems(NIPS), 2004: 1141-1148.

    [26] Pauca V P, Piper J, Plemmons R J. Nonnegative matrix factorization for spectral data analysis[J]. Linear Algebra & Its Applications, 2006, 416(1):29-47.

    [27] Lee Y L, Andrews M.Blind spectral unmixing for compressive hyperspectral imaging of highly mixed data[C]//IEEE International Conference on Image Processing, IEEE,2015:1312-1316.

    [28] Pang Q, Yu J, Sun W. A spectral unmixing method based on wavelet weighted similarity[C]// IEEE International Conference on Image Processing. IEEE, 2015:1865-1869.

    [29] Li H, Li S, Zhang L.Adaptive endmember extraction based sparse nonnegative matrix factorization with spatial local information[C]//IEEE Geoscience and Remote Sensing Symposium, IEEE,2015:1753-1756.

    [30] Fu X, Ma W K, Huang K, et al. Robust volume minimization-based matrix factorization via alternating optimization[C]// IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2016:2534-2538.

    [31] Liu J, Wu Z, Wei Z, et al.A novel sparsity constrained nonnegative matrix factorization for hyperspectral unmixing[C]// IEEE Geoscience and Remote Sensing Symposium, IEEE,2012:1389-1392.

    [32] Wang N, Du B, Zhang L.An Endmember Dissimilarity Constrained Non-Negative Matrix Factorization Method for Hyperspectral Unmixing[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2013, 6(2):554-569.

    [33] Chang C I. Spectral information divergence for hyperspectral image analysis[C]// Geoscience and Remote Sensing Symposium, 1999. IGARSS '99 Proceedings. IEEE 1999 International. IEEE, 1999:509-511 vol.1.

    [34] Qian B, Zhou J, Lei T, et al. Nonnegative matrix factorization with endmember sparse graph learning for hyperspectral unmixing[C]// IEEE International Conference on Image Processing. IEEE, 2016:1843-1847.

    [35] Imbiriba T, Borsoi R A, Bermudez J C M. Generalized linear mixing model accounting for endmember variability[EB/OL]. https://arxiv.org/pdf/1710.07723v1.pdf, 2017-10-20.

    [36] Thouvenin P A, Dobigeon N, Tourneret J Y. Hyperspectral Unmixing With Spectral Variability Using a Perturbed Linear Mixing Model[J]. IEEE Transactions on Signal Processing, 2015, 64(2):525-538.

    [37] Miao L, Qi H. Endmember Extraction From Highly Mixed Data Using Minimum Volume Constrained Nonnegative Matrix Factorization[J].IEEE Transactions on Geoscience & Remote Sensing, 2007, 45(3):765-777.

    [38] Drumetz L, Veganzones M A, Henrot S, et al. Blind hyperspectral unmixing using an Extended Linear Mixing Model to address spectral variability[J].IEEE Transactions Image Process, 2016, 25(8):3890-3905.

    [39] Gao Z G, Zhang L Q. Multi-seasonal spectral characteristics analysis of coastal salt marsh vegetation in Shanghai, China[J]. Estuarine Coastal & Shelf Science, 2006, 69(1):217-224.

    [40] Lukes P, Stenberg P, Rautiainen M, et al. Optical properties of leaves and needles for boreal tree species in Europe[J]. Remote Sensing Letters, 2013, 4(7):667-676.

    [41] Jia S, Qian Y.Constrained Nonnegative Matrix Factorization for Hyperspectral Unmixing[J],IEEE Transactions on Geoscience & Remote Sensing, 2009, 47(1):161-173.

    [42] Iordache M D, Bioucas-Dias J M, Plaza A.Sparse Unmixing of Hyperspectral Data[J].IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(6):2014-2039.

    [43] W.He,H.Zhang,L.Zhang,H.Shen.Total Variation Regularized Reweighted Sparse Non-Negative Matrix Factorization for Hyperspectral Unmixing[J].IEEE Transactions on Geoscience and Remote Sensing,2017, 55(7):3909-3921.

    [44] Bruckstein A M, Elad M, Zibulevsky M.On the Uniqueness of Nonnegative Sparse Solutions to Underdetermined Systems of Equations[J].IEEE Transactions on Information Theory, 2008, 54(11):4813-4820.

    [45] Qian Y, Jia S, Zhou J, et al.Hyperspectral Unmixing via L0.5 Sparsity-Constrained Nonnegative Matrix Factorization[J].IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11):4282-4297.

    [46] Li X, Zhou J, Tong L, et al.Structured Discriminative Nonnegative Matrix Factorization for hyperspectral unmixing[C]//IEEE International Conference on Image Processing,IEEE,2016:1848-1852.

    [47] Jiang X, Ma L, Yang Y.Cluster constraint based sparse NMF for hyperspectral imagery unmixing[C]//IEEE International Conference on Image Processing,IEEE,2014:5107-5111.

    [48] Yuan Y, Fu M, Lu X.Substance Dependence Constrained Sparse NMF for Hyperspectral Unmixing[J].IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(6):2975-2986.

    [49] Xu Z, Chang X, Xu F, et al.L1/2 regularization: a thresholding representation theory and a fast solver[J].IEEE Transactions on Neural Networks & Learning Systems, 2012, 23(7):1013.

    [50] Wang W, Qian Y,Adaptive L1/2 Sparsity-Constrained NMF With Half-Thresholding Algorithm for Hyperspectral Unmixing[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(6):2618-2631.

    [51] Sigurdsson J, Ulfarsson M O, Sveinsson J R.Hyperspectral Unmixing With Lq Regularization[J].IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(11):6793-6806.

    [52] Zhu F, Wang Y, Fan B, et al.Spectral unmixing via data-guided sparsity[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2014,23(12):5412-27.

    [53] Salehani Y E, Gazor S, Kim I M, et al.0-Norm Sparse Hyperspectral Unmixing Using Arctan Smoothing[J].Remote Sensing, 2016, 8(3):187.

    [54] Li J, Bioucas-Dias J M, Plaza A, et al.Robust Collaborative Nonnegative Matrix Factorization for Hyperspectral Unmixing[J].IEEE Transactions on Geoscience & Remote Sensing, 2016, 54(10):6076-6090.

    [55] Lei T, Zhou J, Li X, et al.Region-Based Structure Preserving Nonnegative Matrix Factorization for Hyperspectral Unmixing[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2017, PP(99):1-14.

    [56] Sigurdsson J, Ulfarsson M O, Sveinsson J R, et al. Sparse distributed hyperspectral unmixing[C]// Geoscience and Remote Sensing Symposium. IEEE, 2016.

    [57] Liu X, Xia W, Wang B, et al. An Approach Based on Constrained Nonnegative Matrix Factorization to Unmix Hyperspectral Data[J].IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(2):757-772.

    [58] Liu J, Zhang J, Gao Y, et al.Enhancing Spectral Unmixing by Local Neighborhood Weights[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(5): 1545-1552.

    [59] Liu R, Du B, Zhang L.Hyperspectral Unmixing via Double Abundance Characteristics Constraints Based NMF[J].Remote Sensing, 2016, 8(6):464.

    [60] Chen C H. Independent component analysis for remote sensing study[J]. Proceedings of SPIE-The International Society for Optical Engineering, 1999, 3871:150-158.

    [61] Liu J, Wu Z, Wei Z, et al.A novel sparsity constrained nonnegative matrix factorization for hyperspectral unmixing[C]// IEEE Geoscience and Remote Sensing Symposium, IEEE,2012:1389-1392.

    [62] Mei S, Bi Q, Ji J, et al. Spectral Variation Alleviation by Low-Rank Matrix Approximation for Hyperspectral Image Analysis[J]. IEEE Geoscience & Remote Sensing Letters, 2016, 13(6):796-800.

    [63] Lu X, Wu H, Yuan Y, et al.Manifold Regularized Sparse NMF for Hyperspectral Unmixing[J].IEEE Transactions on Geoscience & Remote Sensing, 2013, 51(5):2815-2826.

    [64] Zhu F Yang S, Zhang X, Yao Y, et al.Geometric Nonnegative Matrix Factorization (GNMF) for Hyperspectral Unmixing[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(6):2696-2703.

    [65] Wang Y, Xiang S, et al.Structured Sparse Method for Hyperspectral Unmixing[J].Isprs Journal of Photogrammetry & Remote Sensing, 2014, 88(2):101-118.

    [66] Li X, Zhou J, Tong L, et al.Structured Discriminative Nonnegative Matrix Factorization for hyperspectral unmixing[C]//IEEE International Conference on Image Processing,IEEE,2016:1848-1852.

    [67] Tong L, Zhou J, Qian Y, et al. Multiple graph regularized NMF for hyperspectral unmixing[C]// Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. IEEE, 2017:1-8.

    [68] Wang W, Qian Y, Tang Y Y.Hypergraph-Regularized Sparse NMF for Hyperspectral Unmixing[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016, 9(2):681-694.

    [69] Jiang X, Ma L, Yang Y.Cluster constraint based sparse NMF for hyperspectral imagery unmixing[C]//IEEE International Conference on Image Processing,IEEE,2014:5107-5111.

    [70] Qian Y, Xiong F, Zeng S, et al.Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery[J].IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1776-1792.

    [71] Zhang Q, Wang H, Plemmons R J, et al. Tensor methods for hyperspectral data analysis: a space object material identification study[J]. J Opt Soc Am A Opt Image Sci Vis, 2008, 25(12):3001-12.

    [72] Favier G, Almeida A L D. Overview of constrained PARAFAC models[J]. Eurasip Journal on Advances in Signal Processing, 2014, 2014(1):1-25.

    [73] Ma W, Bioucasdias J M, Chan T, et al.A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing[J].IEEE Signal Processing Magazine, 2014, 31(1): 67-81.

    [74] Zhao G, Jia X, Zhao C.Multiple endmembers based unmixing using archetypal analysis[C]. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015: 5039-5042.

    [75] AdeleCutler,LeoBreiman.Archetypal Analysis[J].Technometrics, 1994, 36(4):338-347.

    [76] Zhao C, Zhao G, Jia X.Hyperspectral Image Unmixing Based on Fast Kernel Archetypal Analysis[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(1): 331-346.

    [77] Zhao G, Zhao C, Jia X.Multilayer Unmixing for Hyperspectral Imagery With Fast Kernel Archetypal Analysis[J].IEEE Geoscience and Remote Sensing Letters, 2016, 13(10): 1532-1536.

    [78] Chen P, Nelson J, Tourneret J Y.Towards a Sparse Bayesian Markov Random Field Approach to Hyperspectral Unmixing and Classification[J].IEEE Transactions on Image Processing, 2016, (99):1-1.

    [79] Hahn J T, Zoubir A M,Bayesian Nonparametric Unmixing of Hyperspectral Images.[EB/OL]https://arxiv.org/abs/1702.08007, 2017-02-26.

    [80] Schmidt M N, Winther O, Hansen L K. Bayesian Non-negative Matrix Factorization[C]// International Conference on Independent Component Analysis and Signal Separation. Springer-Verlag, 2009:540-547.

    [81] Arngren M, Schmidt M N, Larsen J, et al.Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior[J].Signal Processing Systems, 2011, 65(3): 479-496.

    [82] Halimi A, Dobigeon N, Tourneret J Y, et al. A new Bayesian unmixing algorithm for hyperspectral images mitigating endmember variability[C]//IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2015:2469-2473.

    [83] Altmann Y, Mclaughlin S, Hero A. Robust linear spectral unmixing using outlier detection[C]// IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2015:2464-2468.

    [84] Eches O, Benediktsson JA, Dobigeon N, et al. Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral images[J]. IEEE Transactions on Image Processing, 2013, 22(1):5-16.

    [85] Seyyedsalehi S F, Rabiee H R, Soltani-Farani A, et al.A Probabilistic Joint Sparse Regression Model for Semisupervised Hyperspectral Unmixing[J].IEEE Geoscience & Remote Sensing Letters, 2017, 14(5):592-596.

    [86] Arngren M, Schmidt M N, Larsen J, et al.Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior[J].Signal Processing Systems, 2011, 65(3): 479-496.

    [87] Eches O, Dobigeon N, Mailhes C, et al. Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery[J]. IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society, 2010, 19(6):1403.

    [88] Mittelman R, Dobigeon N, Hero A O.Hyperspectral Image Unmixing Using a Multiresolution Sticky HDP[J].IEEE Transactions on Signal Processing, 2012, 60(4):1656-1671.

    [89] Eches O, Benediktsson JA, Dobigeon N, et al. Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral images[J]. IEEE Transactions on Image Processing, 2013, 22(1):5-16.

    [90] Eches O, Dobigeon N, Tourneret J Y. Enhancing Hyperspectral Image Unmixing With Spatial Correlations[J]. IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(11):4239-4247.

    [91] Zare A, Ho K C.Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing[J].IEEE Signal Processing Magazine, 2014, 31(1):95-104.

    [92] Wu R, Ma W K, Fu X.A stochastic maximum-likelihood framework for simplex structured matrix factorization[C]//IEEE International Conference on Acoustics, Speech and Signal Processing,IEEE, 2017:2557-2561.

    [93] Deng S, Xu Y, Li X, et al.An infinite Gaussian mixture model with its application in hyperspectral unmixing[J].Expert Systems with Applications, 2015, 42(4):1987-1997.

    [94] Zhuang L, Bing Z, Gao L, et al.Normal Endmember Spectral Unmixing Method for Hyperspectral Imagery[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(6):2598-2606.

    [95] Nascimento J M P, Bioucas-Dias J M.Hyperspectral unmixing based on mixtures of Dirichlet components[J].IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(3): 863-878.

    [96] Hahn J T, Zoubir A M,Bayesian Nonparametric Unmixing of Hyperspectral Images.[EB/OL]https://arxiv.org/abs/1702.08007, 2017-02-26.

    [97] Themelis K E, Rontogiannis A A, Koutroumbas K D.A Novel Hierarchical Bayesian Approach for Sparse Semisupervised Hyperspectral Unmixing[J].IEEE Transactions on Signal Processing, 2012, 60(2):585-599.

    [98] Figliuzzi B, Velasco-Forero S, Bilodeau M, et al. A Bayesian Approach to Linear Unmixing in the Presence of Highly Mixed Spectra[C]// International Conference on Advanced Concepts for Intelligent Vision Systems. Springer International Publishing, 2016:263-274.

    [99] Somers B, Zortea M, Plaza A, et al.Automated Extraction of Image-Based Endmember Bundles for Improved Spectral Unmixing[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2012, 5(2):396-408.

    [100] Gao L, Zhuang L, Zhang B.Region-Based Estimate of Endmember Variances for Hyperspectral Image Unmixing[J].IEEE Geoscience & Remote Sensing Letters, 2016, PP(99):1-5.

    [101] Du X, Zare A, Gader P, et al.Spatial and Spectral Unmixing Using the Beta Compositional Model[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2014, 7(6):1994-2003.

    [102] Zhou Y, Rangarajan A, Gader P D.A Gaussian mixture model representation of endmember variability for spectral unmixing[C]. IEEE 2016 Eighth IEEE Grss Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

    [103] Clark R N, Swayze G A, Wise R, et al. USGS digital spectral library splib06a[J]. Data, 2007.

    [104] Tang W, Shi Z, Wu Y, et al.Sparse Unmixing of Hyperspectral Data Using Spectral A Priori Information[J].IEEE Transactions on Geoscience & Remote Sensing, 2016, 53(2):770-783.

    [105] Yang J, Zhao Y Q, Chan J C W, et al.Coupled sparse denoising and unmixing with low-rank constraint for hyperspectral image[J].IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3): 1818-1833.

    [106] Chen F, Zhang Y.Sparse Hyperspectral Unmixing Based on Constrained lp-l2 Optimization[J].IEEE Geoscience & Remote Sensing Letters, 2013, 10(5):1142-1146.

    [107] Iordache M D, Bioucas-Dias J M, Plaza A.Collaborative Sparse Regression for Hyperspectral Unmixing[J].IEEE Transactions on Geoscience & Remote Sensing, 2014, 52(1):341-354.

    [108] Wang D, Shi Z, Tang W.Collaborative sparse unmixing of hyperspectral data using L2, P norm[C]//IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE,2016: 6978-6981.

    [109] Sun L, Jeon B, Zheng Y, et al.Hyperspectral unmixing based on L1-L2 sparsity and total variation[C]//IEEE International Conference on Image Processing, IEEE,2016:4349-4353.

    [110] Wang R, Li H C, Liao W, et al.Double reweighted sparse regression for hyperspectral unmixing[C]// IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE,2016: 6986-6989.

    [111] Giampouras P V, Themelis K E, Rontogiannis A A, et al. Simultaneously Sparse and Low-Rank Abundance Matrix Estimation for Hyperspectral Image Unmixing[J]. IEEE Transactions on Geoscience & Remote Sensing, 2016, 54(8):4775-4789.

    [112] Ma Y, Li C, Mei X, et al.Robust Sparse Hyperspectral Unmixing With L21 Norm[J].IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(3): 1227-1239.

    [113] Aggarwal H K, Majumdar A.Hyperspectral unmixing in the presence of mixed noise using joint-sparsity and total variation[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(9): 4257-4266.

    [114] Li C, Chen X, Jiang Y.On Diverse Noises in Hyperspectral Unmixing[J].IEEE Transactions on Geoscience & Remote Sensing, 2015, 53(10):5388-5402.

    [115] Li C, Zhou A, Zhang G, et al.An Antinoise Method for Hyperspectral Unmixing[J].IEEE Geoscience & Remote Sensing Letters, 2015, 12(3):636-640.

    [116] Wang Y, Pan C, Xiang S, et al.Robust hyperspectral unmixing with correntropy-based metric[J].IEEE Transactions on Image Processing, 2015, 24(11): 4027-4040.

    [117] Zhu F, Halimi A, Honeine P, et al.ADMM for maximum correntropy criterion[C]. IEEE International Joint Conference on Neural Networks, 2016:1420-1427.

    [119] Meyer T R, Drumetz L, Chanussot J, et al. Hyperspectral unmixing with material variability using social sparsity[C]// IEEE International Conference on Image Processing. IEEE, 2016:2187-2191.

    [120] Zheng C Y, Li H, Wang Q, et al.Reweighted Sparse Regression for Hyperspectral Unmixing[J].IEEE Transactions on Geoscience & Remote Sensing, 2015, 54(1):479-488.

    [121] Iordache M D, Bioucas-Dias J M, Plaza A. Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing[J]. IEEE Transactions on Geoscience & Remote Sensing, 2012, 50(11):4484-4502.

    [122] Xu N, Xiao X, Geng X, et al.Spectral-spatial constrained sparse unmixing of hyperspectral imagery using a hybrid spectral library[J].Remote Sensing Letters, 2016, 7(7):641-650.

    [123] Rizkinia M, Okuda M. Local abundance regularization for hyperspectral sparse unmixing[C]// Signal and Information Processing Association Summit and Conference. IEEE, 2017:1-6.

    [124] Yingying Xu, Faming Fang, Guixu Zhang. Similarity-Guided and Lp-Regularized Sparse Unmixing of Hyperspectral Data[J]. IEEE Geoscience & Remote Sensing Letters, 2015, 12(11):2311-2315.

    [125] Bieniarz J, Aguilera E, Zhu X X, et al.Joint Sparsity Model for Multilook Hyperspectral Image Unmixing[J].IEEE Geoscience & Remote Sensing Letters, 2014, 12(4):696-700.

    [126] Yuan Y, Feng Y, Lu X.Projection-Based NMF for Hyperspectral Unmixing[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2015, 8(6):2632-2643.

    [127] Zhong Y, Feng R, Zhang L.Non-Local Sparse Unmixing for Hyperspectral Remote Sensing Imagery[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2014, 7(6):1889-1909.

    [128] Feng R, Zhong Y, Zhang L. An Improved Nonlocal Sparse Unmixing Algorithm for Hyperspectral Imagery[J]. IEEE Geoscience & Remote Sensing Letters, 2015, 12(4):915-919.

    [129] Feng R, Zhong Y, Zhang L. Adaptive Spatial Regularization Sparse Unmixing Strategy Based on Joint MAP for Hyperspectral Remote Sensing Imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016, 9(12):5791-5805.

    [130] Wang R, Li H C, Liao W, et al.Centralized Collaborative Sparse Unmixing for Hyperspectral Images[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2017, PP(99):1-14.

    YUAN Jing, ZHANG Yu-Jin, GAO Fang-Ping. An overview on linear hyperspectral unmixing[J]. Journal of Infrared and Millimeter Waves, 2018, 37(5): 553
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