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The aim of this paper is to apply genuine hyperspectral mathematical morphology to extract spatial structures according to a set of reference spectra. To achieve this objective, a vector multiclass ordering is introduced in this paper. The proposed ordering is based on a supervised framework which requires a training set. This multiclass supervised ordering may then used for the extension of mathematical...
The aim of this paper is to apply genuine hyperspectral mathematical morphology to extract spatial structures according to their spectral nature. To achieve this objective, a novel approach for vectorial ordering is introduced in this paper. The proposed ordering is based on a supervised framework which requires a reference spectrum for the image background and, at least, another reference spectrum...
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