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

Bobin, J. and Moudden, Y. and Starck, J.-L. and Fadili, J. (2009) Sparsity constraints for hyperspectral data analysis: linear mixture model and beyond. In: Wavelets XIII. Proceedings of SPIE. No.7446. Society of Photo-Optical Instrumentation Engineers (SPIE) , Bellingham, WA, Art. No. 74461D. ISBN 9780819477361. https://resolver.caltech.edu/CaltechAUTHORS:20161129-132926258

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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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1117/12.826131DOIArticle
http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1341134PublisherArticle
Additional Information:© 2009 SPIE--The International Society for Optical Engineering. This work was partially supported by the French National Agency for Research (ANR -08-EMER-009-01). The authors are grateful to Olivier Forni for providing the hyperspectral data from Omega on Mars Express.
Funders:
Funding AgencyGrant Number
Agence Nationale pour la Recherche (ANR)ANR-08-EMER-009-01
Subject Keywords:Morphological diversity, sparsity, overcomplete representation, curvelets, wavelets, multichannel data, blind source separation, denoising, inpainting, multichannel convolution
Series Name:Proceedings of SPIE
Issue or Number:7446
Record Number:CaltechAUTHORS:20161129-132926258
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20161129-132926258
Official Citation:J. Bobin ; Y. Moudden ; J.-L. Starck and J. Fadili "Sparsity constraints for hyperspectral data analysis: linear mixture model and beyond", Proc. SPIE 7446, Wavelets XIII, 74461D (September 04, 2009); doi:10.1117/12.826131; http://dx.doi.org/10.1117/12.826131
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72411
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:29 Nov 2016 21:59
Last Modified:03 Oct 2019 16:17

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