Sparsity constraints for hyperspectral data analysis: linear mixture model and beyond
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.
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.Attached Files
Published - 74461D_1.pdf
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Additional details
- Eprint ID
- 72411
- Resolver ID
- CaltechAUTHORS:20161129-132926258
- ANR-08-EMER-009-01
- Agence Nationale pour la Recherche (ANR)
- Created
-
2016-11-29Created from EPrint's datestamp field
- Updated
-
2021-11-11Created from EPrint's last_modified field
- Series Name
- Proceedings of SPIE
- Series Volume or Issue Number
- 7446