Nonlinear spectral unmixing for hyperspectral imagery based on bilinear mixture models

YANG Bin; WANG Bin; WU Zong-Min

doi:10.11972/j.issn.1001-9014.2018.05.017

[1] Bioucas-Dias J M, 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(2):354-379.

[2] Tong Q, Xue Y, Zhang L. Progress in hyperspectral remote sensing science and technology in china over the past three decades ［J］. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2014, 7(1): 70-91.

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

[4] Heylen R, Parente M, Gader P. A review of nonlinear hyperspectral unmixing methods ［J］. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2014,7(6):1844-1868.

[5] Dobigeon N, Tourneret J, Richard C, et al. Nonlinear unmixing of hyperspectral images: models and algorithms ［J］. IEEE Signal Process. Mag., 2014, 31(1):82-94.

[6] Hapke B W. Bidirectional reflectance spectroscopy. I. Theory ［J］. J. Geophys. Res., 1981, 86: 3039-3054.

[7] Nascimento J M P, Bioucas-Dias J M. Nonlinear mixture model for hyperspectral unmixing ［J］. in Proc. Soc. Photo-Opt. Instrum. Eng. (SPIE), Image Signal Process. Remote Sens. XV, 2009, 7477:1-8.

[8] Fan W, Hu B, Miller J, et al. Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated forest hyperspectral data ［J］. Int. J. Remote Sens., 2009, 30(11): 2951-2962.

[9] Halimi A, Altmann Y, Dobigeon N, et al. Nonlinear unmixing of hyperspectral images using a generalized bilinear model ［J］. IEEE Trans. Geosci. Remote Sens., 2011, 49(11): 4153-4162.

[10] Altmann Y, Halimi A, Dobigeon N, et al. Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery ［J］. IEEE Trans. Image Process., 2012, 21(6):3017-3025.

[11] Somers B, Tits L, Coppin P. Quantifying nonlinear spectral mixing in vegetated areas: Computer simulation model validation and first results ［J］. IEEE J. Sel. Topics Appl. Earth Observ., 2014, 7(6):1956-1965.

[12] Dobigeon N, Tits L, Somers B, et al. A comparison of nonlinear mixing models for vegetated areas using simulated and real hyperspectral data ［J］. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2014, 7(6): 1869-1878.

[13] Heylen R, Scheunders P. A multilinear mixing model for nonlinear spectral unmixing ［J］. IEEE Trans. Geosci. Remote Sens., 2015, 54(1): 240-251.

[14] Marinoni A, Gamba P. A novel approach for efficient p-linear hyperspectral unmixing ［J］. IEEE J. Sel. Signal Process, 2015, 9(6):1156-1168.

[15] Nascimento J M P, Dias J M B. Vertex component analysis: A fast algorithm to unmix hyperspectral data ［J］. IEEE Trans. Geosci. Remote Sens., 2005, 43(4):898-910.

[16] Heylen R, Scheunders P. Calculation of geodesic distances in non-linear mixing models: application to the generalized bilinear model［J］. IEEE Geosci. Remote Sens. Lett., 2012, 9(4):644-648.

[17] Eches O, Guillaume M. A bilinear-bilinear nonnegative matrix factorization method for hyperspectral unmixing ［J］. IEEE Geosci. Remote Sens. Lett., 2014, 11(4):778-782.

[18] Yokoya N, Chanussot J, Iwasaki A. Nonlinear unmixing of hyperspectral data using semi-nonnegative matrix factorization ［J］. IEEE Trans. Geosci. Remote Sens., 2014, 52(2):1430-1437.

[19] Pu H, Chen Z, Wang B, et al. Constrained least squares algorithms for nonlinear unmixing of hyperspectral imagery ［J］. IEEE Trans. Geosci. Remote Sens., 2015, 53(3):1287-1303.

[20] Li C, Ma Y, Huang J, et al. GBM-based unmixing of hyperspectral data using bound projected optimal gradient method ［J］. IEEE Geosci. Remote Sens. Lett., 2016, 13(7):952-956.

[21] Li J, Li J, Huang B, et al. Hopfield neural network approach for supervised nonlinear spectral unmixing ［J］. IEEE Geosci. Remote Sens. Lett., 2016, 13(7):1002-1006.

[22] Luo W, Gao L, Plaza A, et al. A new algorithm for bilinear spectral unmixing of hyperspectral images using particle swarm optimization ［J/OL］. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 2016. http://ieeexplore.ieee.org/document/7569014/.

[23] Cui J, Li X, Zhao L. Nonlinear spectral mixture analysis by determining per-pixel endmember sets ［J］. IEEE Geosci. Remote Sens. Lett., 2014, 11(8):1404-1408.

[24] Qu Q, Nasrabadi N M, Tran T D. Abundance estimation for bilinear mixture models via joint sparse and low-rank representation ［J］. IEEE Trans. Geosci. Remote Sens., 2014, 52(7): 4404-4423.

[25] Ma L, Chen J, Zhou Y, et al. Two-step constrained nonlinear spectral mixture analysis method for mitigating the collinearity effect ［J］. IEEE Trans. Geosci. Remote Sens., 2016, 54(5): 2873-2886.

[26] Heinz D C, 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.

[27] Broadwater J, Chellappa R, Banerjee A, et al. Kernel fully constrained least squares abundance estimates ［C］. Barcelona: 2007.

[28] Bioucas-Dias J M, Nascimento J M P. Hyperspectral subspace identification ［J］. IEEE Trans. Geosci. Remote Sens., 2008, 46(8): 2435-2445.

[29] Swayze G, Clark R, Sutley S, et al. Ground-truthing AVIRIS mineral mapping at Cuprite, Nevada［J］. Summaries of the Third Annual JPL Airborne Geosciences Workshop,1992, AVIRIS Workshop JPL Publication: 47-49.

[30] Bethel J, Lee C, Landgrebe D A. Geometric registration and classification of hyperspectral airborne pushbroom data［J］. International Archives of Photogrammetry and Remote Sensing, 2000, 33:183-190.

CLP Journals