On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation

Authors:
S. Moussaoui;H. Hauksdóttir;F. Schmidt;C. Jutten;J. Chanussot;D. Brie;S. Douté;J. A. Benediktsson
Affiliations:
Institut de Recherche en Communications et Cybernétique de Nantes, 1 rue de la Noë, BP 92101, F-44321 Nantes Cedex 3, France;Department of Electrical and Computer Engineering, University of Iceland, Hjardarhaga 2-6, 107 Reykjavík, Iceland and Laboratoire GIPSA, INPG, 46 avenue Félix Viallet, F-38031 Grenoble C ...;Laboratoire de Planétologie de Grenoble, Batiment D de Physique, BP 53, F-38041 Saint-Martin d'Hères Cedex 9, Grenoble, France;Laboratoire GIPSA, INPG, 46 avenue Félix Viallet, F-38031 Grenoble Cedex, France;Laboratoire GIPSA, INPG, 46 avenue Félix Viallet, F-38031 Grenoble Cedex, France;Centre de Recherche en Automatique de Nancy, B.P. 239, Boulevard des Aiguillettes, F-54500, Vandoeuvre-lès-Nancy Cedex, France;Laboratoire de Planétologie de Grenoble, Batiment D de Physique, BP 53, F-38041 Saint-Martin d'Hères Cedex 9, Grenoble, France;Department of Electrical and Computer Engineering, University of Iceland, Hjardarhaga 2-6, 107 Reykjavík, Iceland
Venue:
Neurocomputing
Year:
2008

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Abstract

The surface of Mars is currently being imaged with an unprecedented combination of spectral and spatial resolution. This high resolution, and its spectral range, gives the ability to pinpoint chemical species on the surface and the atmosphere of Mars more accurately than before. The subject of this paper is to present a method to extract informations on these chemicals from hyperspectral images. A first approach, based on independent component analysis (ICA) [P. Comon, Independent component analysis, a new concept? Signal Process. 36 (3) (1994) 287-314], is able to extract artifacts and locations of CO"2 and H"2O ices. However, the main independence assumption and some basic properties (like the positivity of images and spectra) being unverified, the reliability of all the independent components (ICs) is weak. For improving the component extraction and consequently the endmember classification, a combination of spatial ICA with spectral Bayesian positive source separation (BPSS) [S. Moussaoui, D. Brie, A. Mohammad-Djafari, C. Carteret, Separation of non-negative mixture of non-negative sources using a Bayesian approach and MCMC sampling, IEEE Trans. Signal Process. 54 (11) (2006) 4133-4145] is proposed. To reduce the computational burden, the basic idea is to use spatial ICA yielding a rough classification of pixels, which allows selection of small, but relevant, number of pixels. Then, BPSS is applied for the estimation of the source spectra using the spectral mixtures provided by this reduced set of pixels. Finally, the abundances of the components are assessed on the whole pixels of the images. Results of this approach are shown and evaluated by comparison with available reference spectra.