A fully constrained linear unmixing method: Simplex regularization for hyperspectral imagery

XU Ning; GENG Xiu-Rui; YOU Hong-Jian; CAO Yin-Gui

doi:10.11972/j.issn.1001-9014.2016.05.014

[2] BIOUCAS-DIAS J.M., PLAZA A., DOBIGEON N.. 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] KESHAVA N, MUSTARD J F. Spectral unmixing[J]. IEEE Signal Process. Mag., 2002, 19(1):44-57.

[4] HEINZ D C, CHANG C I. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J]. IEEE Trans. Geosci. Remote Sensing., 2001, 39(3):529-545.

[6] BAYLISS J, GUALTIERI J A, CROMP R. Analysing hyperspectral data with independent component analysis[C], in Proc. SPIE, 1997, 3240:133-143.

[7] BOARDMAN J W. Automating spectral unmixing of AVIRIS data using convex geometry concepts[C].In 4th Annual JPL Airborne Geo ScienceWorkshop .Pasadena: JPL Pub , 1993.

[9] HONEINE P, RICHARD C. Geometric unmixing of large hyperspectral images: A barycentric coordinate approach[J]. IEEE Trans. Geosci.Remote Sens., 2012, 50(6):2185-2195.

[11] PU Han-Ye, XIA Wei, WANG Bin, , et al. A Fully Constrained Linear Spectral Unmixing Algorithm Based on Distance Geometry[J]. IEEE Trans. Geosci.Remote Sens,2014, 52(2):1157-1176.

[12] WINTER M E. Fast autonomous spectral endmember determination in hyperspectral data[C]. In: Paper Presented at the Proceedings of SPIE. 1999.

[13] CHANG C I, WU Chao-Cheng, LIU Wei-Ming,et al.A new growing method for simplex-based endmember extraction algorithm[J]. IEEETrans. Geosci. Remote Sens., 2006, 44(10): 2804-2819.

[14] CRAIG M. Minimum-volume transforms for remotely sensed data[J]. IEEE Trans. Geosci. Remote Sens. 1994, 32(3):542-552.

[15] AUDREOU C, KARATHANASSI V. Estimation of the number of endmembers using robust outlier detection method[J].IEEE JournalofSelected Topics in Applied Earth Observationsand Remote Sensing. 2014,7(1):247-256.

[16] YANG Hua-Dong, AN Ju-Bai, ZHU Chuang. Subspace-projection-based geometric unmixing for material quantification in hyperspectral imagery[J]. IEEE JournalofSelected Topics in Applied Earth Observationsand Remote Sensing, 2014, 7(6):1966-1975.

[17] CHAN T H, MA W K, CHI C Y, , et al. A convex analysis framework for blind separation of non-negative sources[J]. IEEE Trans.Signal Process. 2008, 56:5120-5134.

[18] GENG Xiu-Rui, JI Lu-Yan, ZHAO Yong-Chao, et al. A new endmember generation algorithm based on a geometric optimization model for hyperspectral images[J]. IEEE Geosciencesand Remote Sensing Letters. 2013, 10(4):811-815.