Mars Hyperspectral Data Processing using ICA and Bayesian Positive Source Separation

Details Mars Hyperspectral Data Processing using ICA and Bayesian Positive Source Separation Mars Hyperspectral Data Processing using ICA and Bayesian Positive Source Separation

Nov 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006aipc..872..113h&link_type=abstract

Bayesian Inference and Maximum Entropy Methods In Science and Engineering. AIP Conference Proceedings, Volume 872, pp. 113-122

Computer Science

Image Processing, Image Processing, Spectral Methods, Inference Methods

Scientific paper

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) and spectral Bayesian Positive Source Separation (BPSS). 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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