Mars Hyperspectral Data Processing using ICA and Bayesian Positive Source Separation

Abstract : The surface of Mars is currently being mapped with an unprecedented spatial resolution. This high resolution, and its spectral range, give the ability to pinpoint chemical species on Mars more accurately than before. The subject of this paper is to present a method to extract informations on chemicals using hyperspectral images. We propose to combine spatial Independent Component Analysis (ICA) [1] and spectral Bayesian Positive Source Separation (BPSS) [2]. The basic idea is to use spatial ICA yielding a rough classification of pixels, which allows selection of small, but relevant, number of pixels. BPSS is then applied for the estimation of the source spectra using this reduced set of pixels. Finally, the abundances of the components is assessed on the whole pixels of the images. Results of this approach are shown and evaluated by comparison with reference spectra.
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https://hal.archives-ouvertes.fr/hal-00121596
Contributor : David Brie <>
Submitted on : Thursday, December 21, 2006 - 12:06:55 PM
Last modification on : Friday, September 6, 2019 - 3:00:06 PM

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  • HAL Id : hal-00121596, version 1

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Hafrun Hauksdottir, Saïd Moussaoui, Frédéric Schmidt, Christian Jutten, Jocelyn Chanussot, et al.. Mars Hyperspectral Data Processing using ICA and Bayesian Positive Source Separation. Jul 2006, pp.288-295. ⟨hal-00121596⟩

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