LAUSR

Algorithms allowing the deconvolution of hyperspectral data play a key-role in remotely sensed data processing for mineralogical investigation. Modified Gaussian model (MGM) based methods are of particular interest because they are able to retrieve accurate estimates of minerals abundances and chemistry in surface's rocks. However, MGM-based frameworks deliver high computational complexity and sensitivity to initial parameters for statistical distribution definition. In this paper, a new approach for efficient and robust mineralogical investigation over extraterrestrial bodies is introduced. The proposed framework takes advantage of the solid characterization of remote sensing hyperspectral images by unmixing higher order nonlinear combinations of reflectance features associated with mafic minerals. Experimental results achieved over Mars and Moon hyperspectral images show that the proposed scheme is able to retrieve magmatic mineral abundance maps that are highly correlated to those achieved by means of MGM-based scheme while overcoming the aforesaid issues. Finally, an empirical study allowing to distinguish between clino- and orthopyroxenes by properly processing the outcomes of nonlinear hyperspectral unmixing method is reported.