Aptoula,E.Courty,N.Lefevre,S.2024-05-252024-05-2520145978-147995751-410.1109/ICIP.2014.70260322-s2.0-84949927584https://doi.org/10.1109/ICIP.2014.7026032https://hdl.handle.net/20.500.14517/2309Despite the popularity of mathematical morphology with remote sensing image analysis, its application to hyperspectral data remains problematic. The issue stems from the need to impose a complete lattice structure on the multi-dimensional pixel value space, that requires a vector ordering. In this article, we introduce such a supervised ordering relation, which conversely to its alternatives, has been designed to be image-specific and exploits the spectral purity of pixels. The practical interest of the resulting multivariate morphological operators is validated through classification experiments where it achieves state-of-the-art performance. © 2014 IEEE.eninfo:eu-repo/semantics/openAccessclassificationend-membershyperspectral imagesMathematical morphologyvector orderingConference Object50975101