Morphological processing of hyperspectral images using kriging-based supervised ordering
A novel approach for vectorial 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. In particular, we consider here a kriging-based vectorial ordering. This supervised ordering may then used for the extension of mathematical morphology to vectorial images. Application of morphological processing to hyperspectral image illustrates the performance of proposal operators.