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Stratégies de rastérisation pour la classification de données LiDAR aéroportées par profils d'attributs morphologiques *

Florent Guiotte 1, 2 Sébastien Lefèvre 2 Thomas Corpetti 1, 2
1 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
LETG - Littoral, Environnement, Télédétection, Géomatique UMR 6554
2 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : This paper evaluates rasterization strategies and the benefit of hierarchical representations (in particular attribute profiles) to classify point clouds. When such data comes from LiDAR acquisitions, a rasterization process if often performed to build an elevation map (possibly used together with multi or hyperspectral images). While some works use attribute profiles on such elevation data, we rather focus here on several LiDAR features rasterized and on their multilevel description to produce accurate land cover maps over urban areas. Our experimental results obtained on LiDAR data from the university of Houston indicate good classification results based on our rasters.
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Submitted on : Wednesday, November 13, 2019 - 6:27:50 PM
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Florent Guiotte, Sébastien Lefèvre, Thomas Corpetti. Stratégies de rastérisation pour la classification de données LiDAR aéroportées par profils d'attributs morphologiques *. Colloque GRETSI sur le Traitement du Signal et des Images, 2019, Lille, France. ⟨hal-02343958⟩

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