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The Support Vector Machine provides a new way to design classiication algorithms which learn from examples (supervised learning) and generalize when applied to new data. We demonstrate its success on a diicult classiication problem from hyperspectral remote sensing, where we obtain performances of 96%, and 87% correct for a 4 class problem, and a 16 class(More)
Hyperspectral image sensors provide images with a large number of contiguous spectral channels per pixel and enable information about diierent materials within a pixel to be obtained. The problem of spectrally unmixing materials may be viewed as a speciic case of the blind source separation problem where data consists of mixed signals (in this case(More)
— Hyperspectral imaging offers the possibility of characterizing materials and objects in the air, land and water on the basis of the unique reflectance patterns that result from the interaction of solar energy with the molecular structure of the material. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral data(More)
One of the most widely used approaches to analyze hyperspectral data is pixel unmixing, which relies on the identification of the purest spectra from the data cube. Once these elements, known as " endmembers " , are extracted, several methods can be used to map their spatial distributions, associations and abundances. A large variety of methodologies have(More)