Rasterization strategies for airborne LiDAR classification using attribute profiles

Florent Guiotte (1, 2) , Sébastien Lefèvre (2) , Thomas Corpetti (1, 2)

1 LETG - Littoral, Environnement, Télédétection, Géomatique UMR 6554
2 OBELIX - Observation de l’environnement par imagerie complexe

This paper evaluates rasterization strategies and the benefit of hierarchical representations, in particular attribute profiles, to classify urban scenes issued from multispectral LiDAR acquisitions. In recent years it has been found that rasterized LiDAR provides a reliable source of information on its own or for fusion with multispectral/hyperspectral imagery. However previous works using attribute profiles on LiDAR rely on elevation data only. Our approach focuses on several LiDAR features rasterized with multilevel description to produce precise land cover maps over urban areas. Our experimental results obtained with LiDAR data from university of Houston indicate good classification results for alternative rasters and even more when multilevel image descriptions are used.

Mots clés: land cover mapping; attribute profiles; multilevel image description

Domaines: Traitement des images [eess.IV]

Florent Guiotte, Sébastien Lefèvre, Thomas Corpetti. Rasterization strategies for airborne LiDAR classification using attribute profiles. 2019 Joint Urban Remote Sensing Event (JURSE), May 2019, Vannes, France. pp.1-4, ⟨10.1109/JURSE.2019.8808945⟩. ⟨hal-02343901v2⟩