Aptoula, E.Courty, N.Lefevre, S.2024-10-152024-10-15201497814799575141522-4880https://hdl.handle.net/20.500.14517/6360Aptoula, Erchan/0000-0001-6168-2883; Lefevre, Sebastien/0000-0002-2384-8202Despite 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.eninfo:eu-repo/semantics/openAccessMathematical morphologyvector orderinghyperspectral imagesend-membersclassificationConference Object50975101WOS:0003700636050532