Blind Source Separation with Spatio-Spectral Sparsity Constraints --- Application to Hyperspectral Data Analysis

Authors:
Yassir Moudden;Jerome Bobin;Jean-Luc Starck;Jalal Fadili
Affiliations:
DSM /IRFU/SEDI, CEA/Saclay, Gif-sur-Yvette, France F-91191;DSM /IRFU/SEDI, CEA/Saclay, Gif-sur-Yvette, France F-91191 and Department of Applied & Computational Mathematics, California Institute of Technology, Pasadena, 91125;DSM /IRFU/SEDI, CEA/Saclay, Gif-sur-Yvette, France F-91191;GREYC CNRS UMR 6072, Image Processing Group, ENSICAEN 14050, Caen Cedex, France
Venue:
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Year:
2009

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Abstract

Recently, sparsity and morphological diversity have emerged as a new and effective source of diversity for Blind Source Separation giving rise to novel methods such as Generalized Morphological Component Analysis. The latter takes advantage of the very sparse representation of structured data in large overcomplete dictionaries, to separate sources based on their morphology. Building on GMCA, the purpose of this contribution is to describe a new algorithm for hyperspectral data processing. It assumes the collected data exhibits sparse spectral signatures in addition to sparse spatial morphologies, in specified dictionaries of spectral and spatial waveforms. Numerical experiments are reported which demonstrate the validity of the proposed extension.