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Analysis of a collection of planetary hyperspectral images through non-negative source separation

In typical hyperspectral images encountered in Earth and Planetary Sciences, the spatial extent of a pixel is usually large enough to contain a mixture of various surface/atmospheric constituents which contribute to a single pixel spectrum. Unsupervised spectral unmixing aims at identifying the spectral signatures of materials present in an image and at estimating their abundances in each pixel. Bayesian Prior Source Separation (BPSS) [1] is an interesting way to deal with this unmixing challenge under linearity constraints. Notably, it ensures the non-negativity of both the unmixed component spectra and their abundances, such a constraint being crucial to the physical interpretability of the results. A sum-to-one constraint can also be imposed on the estimated abundances [2] (its relevance depends on the nature of the dataset under consideration). Thanks to adequate implementation strategies [3], computational issues (due to the nature of the algorithm and to the large size of the data) have been overcome, and a pixel selection method, performed as a pre-processing step, also allowed to reach significantly lower computation times without inducing a strong loss of quality as regards the estimation of the sources. As this selection aims at extracting a few especially relevant pixels among all the image pixels, it also contributes to the identification of the most interesting sources present in the analyzed image.