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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...
A novel approach for vector ordering is introduced in this paper. The generic framework is based on a supervised learning formulation which leads to reduced orderings. A training set for the background and another training set for the foreground are needed as well as a supervised method to construct the ordering mapping. Two particular cases of learning techniques are considered in detail: 1) kriging-based...
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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