A classwise supervised ordering approach for morphology based hyperspectral image classification

We present a new method for the spectral-spatial classification of hyperspectral images, by means of morphological features and manifold learning. In particular, mathematical morphology has proved to be an invaluable tool for the description of remote sensing images. However, its application to hyperspectral data is problematic, due to the absence of a complete lattice structure at higher dimensions. We address this issue by following up previous experimental indications on the interest of classwise orderings. The practical interest of the proposed approach is shown through comparison on the Pavia dataset with Extended Morphological Profiles, against which it achieves superior results.