Conventional analyses of soil characteristic are expensive and time-consuming. Hyperspectral remote sensing has become useful tool for quantitative analysis of soil properties particularly in area where soil surface is permanently or temporarily exposed, as in Mediterranean region. Some multivariate statistical methods have been successful in soil spectrometry but they seem to have some limitations. The aim of this work is to identify properties of soils by using an unmixing method, the Support Vectors Machines (SVMs), from Hyperion hyperspectral remote sensing data. The approach consists in i) selection of Hyperion spectra of "extreme" soils among a Hyperion spectra for which soil properties are knows, ii) the application of the SVM to the Hyperion hyperspectral image to classify the pixels. The overall accuracies obtained for the soil characteristic classification are 87,95% (for clay), 73,81% (calcium carbonate, CaCO3) and the Kappa indexes are 0,82 (clay) and 0,60 (CaCO3). Finally, this work has showed that the SVM provides an important and promising perspective in soil science.
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