A Review of the Hyperspectral Unmixing Methods that Based on Constrained NMF and Constrained Sparse Regression

Abstract:

Hyperspectral unmixing (HSU) plays an important role in hyperspectral image analysis, and most of the current HSU algorithms are base on linear mixing model (LMM). This paper gives a review of two linear HSU methods that have been drawn great attention recently: one is constrained nonnegative matrix factorization (CNMF) based method, the other is constrained sparse regression (CSR) based method. We carried on the systematic summary to these two types of methods, based on which, we point out some potential research topics.

• Periodical: Applied Mechanics and Materials (Volumes 713-715)
• Pages: 1540-1545
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.713-715.1540
• Online since: January 2015
• Authors: Cheng Yong Zheng*
• Keywords: Hyperspectral Unmixing, Linear Mixing Model, Nonnegative Matrix Factorization, Sparse Regression

References

[1] Bioucas-Dias J, Plaza A, Dobigeon N, et al. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches[J]. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2012, 5(3): 354-379.

DOI: 10.1109/jstars.2012.2194696

[2] Keshava N, Mustard J. Spectral unmixing[J]. IEEE Signal Process. Mag., 2002, 19(1): 44–57.

DOI: 10.1109/79.974727

[3] Plaza A, Du Q, Bioucas-Dias J, Jia X, et al. Foreword to the special issue on spectral unmixing of remotely sensed data [J]. IEEE Trans. Geosci. Remote Sens., 2011, 49(11): 1–8.

DOI: 10.1109/tgrs.2011.2167193

[4] Heinz D, Chang C. 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.

DOI: 10.1109/36.911111

[5] Lee D D, Seung H S. Algorithms for non-negative matrix factorization [C]/ Advances in neural information processing systems. 2001, MA: MIT Press: Cambridge. pp.556-562.

[6] Hoyer P. Non-negative sparse coding[C]/ 12th IEEE Workshop NNSP, p.557–565, (2002).

[7] Pauca V, Piper J, Plemmons R. Nonnegative matrix factorization for spectral data analysis[J]. Linear Algebra Appl., 2006, 416(1): 29–47.

DOI: 10.1016/j.laa.2005.06.025

[8] 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.

DOI: 10.1109/tgrs.2006.888466

[9] Xu Z, Zhang H, Wang Y, Chang Y. L1/2 regularizer[J]. Sci. China. Ser. F, Inf. Sci., 2011, 53(6): 1159–1169.

[10] Qian Y. Jia S, Zhou J, et al. Hyperspectral unmixing via L1/2 sparsity-constrained nonnegative matrix factorization [J]. IEEE Trans. Geosci. Remote Sens., 2011, 49(11): 4282–4297.

DOI: 10.1109/tgrs.2011.2144605

[11] Jia S, Qian Y. Constrained nonnegative matrix factorization for hyperspectral unmixing [J]. IEEE Trans. Geosci. Remote Sens., 2009, 47(1): 161–173.

DOI: 10.1109/tgrs.2008.2002882

[12] 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 and Remote Sensing, 2013, 6(2): 554-569.

DOI: 10.1109/jstars.2013.2242255

[13] Lu X, Wu H, Yuan Y, et al. Manifold Regularized Sparse NMF for Hyperspectral Unmixing [J]. IEEE Trans. Geosci. Remote Sens., 2013, 51(5): 2815-2826.

DOI: 10.1109/tgrs.2012.2213825

[14] Wang Y, Pan C, Xiang S, et al. Robust Hyperspectral Unmixing with Correntropy based Metric [J]. 2013, arXiv preprint, arXiv: 1305. 7311.

[15] Iordache D, Bioucas-Dias J, Plaza A. Sparse unmixing of hyperspectral data [J]. IEEE Trans. Geosci. Remote Sens., 2011, 49(6): 2014–(2039).

DOI: 10.1109/tgrs.2010.2098413

[16] Bioucas-Dias J, Figueiredo M. Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing [C]/ 2nd Workshop on Hyperspectral Image and Signal IEEE Processing: Evolution in Remote Sensing (WHISPERS). (2010).

DOI: 10.1109/whispers.2010.5594963

[17] Fen C, Yan Z. Sparse Hyperspectral Unmixing Based on Constrained Lp-L2 Optimization [J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5): 1142-1146.

DOI: 10.1109/lgrs.2012.2232901

[18] Sun L, Wu Z, Xiao L, et al., A novel l 1/2 sparse regression method for hyperspectral unmixing[J]. International Journal of Remote Sensing. 2013, 34(20): 6983-7001.

DOI: 10.1080/01431161.2013.804225

[19] Iordache M, Bioucas-Dias J, Plaza A. Total Variation Spatial Regularization for Sparse Hyperspectral Unmixing. IEEE Trans. Geosci. Remote Sens. 2012, 50(11) 4484-4502.

DOI: 10.1109/tgrs.2012.2191590