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Proceedings Paper

Sparsity constraints for hyperspectral data analysis: linear mixture model and beyond
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Paper Abstract

The recent development of multi-channel sensors has motivated interest in devising new methods for the coherent processing of multivariate data. An extensive work has already been dedicated to multivariate data processing ranging from blind source separation (BSS) to multi/hyper-spectral data restoration. Previous work has emphasized on the fundamental role played by sparsity and morphological diversity to enhance multichannel signal processing. GMCA is a recent algorithm for multichannel data analysis which was used successfully in a variety of applications including multichannel sparse decomposition, blind source separation (BSS), color image restoration and inpainting. Inspired by GMCA, a recently introduced algorithm coined HypGMCA is described for BSS applications in hyperspectral data processing. It assumes the collected data is a linear instantaneous mixture of components exhibiting sparse spectral signatures as well as sparse spatial morphologies, each in specified dictionaries of spectral and spatial waveforms. We report on numerical experiments with synthetic data and application to real observations which demonstrate the validity of the proposed method.

Paper Details

Date Published: 4 September 2009
PDF: 15 pages
Proc. SPIE 7446, Wavelets XIII, 74461D (4 September 2009); doi: 10.1117/12.826131
Show Author Affiliations
J. Bobin, California Institute of Technology (United States)
Y. Moudden, CEA, IRFU, SEDI-SAP, Lab. AIM, CEA/DSM, CNRS, Univ. Paris Diderot (France)
J.-L. Starck, CEA, IRFU, SEDI-SAP, Lab. AIM, CEA/DSM, CNRS, Univ. Paris Diderot (France)
J. Fadili, GREYC, CNRS, ENSICAEN (France)

Published in SPIE Proceedings Vol. 7446:
Wavelets XIII
Vivek K. Goyal; Manos Papadakis; Dimitri Van De Ville, Editor(s)

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