Piecewise Convex Multiple-Model Endmember Detection and Spectral Unmixing

Abstract:

A hyperspectral endmember detection and spectral unmixing algorithm that finds multiple sets of endmembers is presented. Hyperspectral data are often nonconvex. The Piecewise Convex Multiple-Model Endmember Detection algorithm accounts for this using a piecewise convex model. Multiple sets of endmembers and abundances are found using an iterative fuzzy clustering and spectral unmixing method. The results indicate that the piecewise convex representation estimates endmembers that better represent hyperspectral imagery composed of multiple regions where each region is represented with a distinct set of endmembers.

 

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Plain Text:

Zare, A.; Gader, P.; Bchir, O.; Frigui, H., “Piecewise Convex Multiple-Model Endmember Detection and Spectral Unmixing,” IEEE Trans. Geosci. Remote Sens., vol. 51, no. 5, pp. 2853-2862, May 2013.

Bibtex:

@ARTICLE{ZareGader:2013,
author={Zare, A. and Gader, P. and Bchir, O. and Frigui, H.},
journal={IEEE Trans. Geosci. Remote Sens.}, title={Piecewise Convex Multiple-Model Endmember Detection and Spectral Unmixing},
year={2013}, month={May},
volume={51},
number={5},
pages={2853-2862},}

 

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