Spatial structures detection in hyperspectral images using mathematical morphology
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 for the image target. This supervised ordering may then used for the extension of mathematical morphology to vectorial images and in particular, we focus here on the application of morphological processing to hyperspectral images, illustrating the performance with real examples.