The surface of Mars is currently being imaged with an unprecedented combination of spectral and spatial resolution. This high resolution, and its spectral range, give the ability to pinpoint chemical species on the surface and the atmosphere of Mars moreaccurately 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) [1], is able to extract artifacts and locations of CO2 and H2O 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) [2] 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 and 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 is assessed on the whole pixels of the images. Results of this approach are shown and evaluated by comparison with available reference spectra.

The truth about what chemicals are to be found on the surface of Mars lies hidden in Gigabytes of hyperspectral data. How to reveal this mystery is the subject of this paper. Independent component analysis (ICA) is used for identification and classification of endmembers and for artifact removal. The classification results are compared with the result of a wavelet classifier and reference spectra are used for identification of known substances. C02 ice and water ice and an intimate mixture of C02 ice and dust are effectively found as independent components, but because of high negative correlation of dust and C02 ice, dust is not found as a separate component. ICA can be used to valuate the atmospheric effect removal, which is currently being used and can help in this preprocessing. ICA can also be used for other artifacts, such as to find and clean corrupted channels and to detect the effect of the overlay of sensors. It is proposed to view the mixing matrix as a collection of independent components (ICs) spectra, and use this for automatic detection of known endmembers.

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.