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A hierarchical deep neural network with iterative features for semantic labeling of airborne LiDAR point clouds / Yetao Yang in Computers & geosciences, vol 157 (December 2021)  

Public [article]  inComputers & geosciences > vol 157 (December 2021) . - n° 104932 Titre : A hierarchical deep neural network with iterative features for semantic labeling of airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Yetao Yang, Auteur ; Rongkui Tang, Auteur ; Jinglei Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104932 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] classification barycentrique [Termes IGN] classification par réseau neuronal [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] itération [Termes IGN] segmentation sémantique [Termes IGN] semis de points [Termes IGN] structure hiérarchique de données Résumé : (auteur) Airborne LiDAR point clouds classification has been a challenging task due to the characteristics of point clouds and the complexity of the urban environment. Recently, methods that directly act on unordered point set have achieved satisfactory results in point clouds classification. However, the existing methods that directly consume point clouds pay little attention to the interaction between the deep layers, which makes the feature learning insufficient in complex environments. In this paper, we propose a deep neural network for semantic labeling task. It iteratively learns deep features in a hierarchical structure, and provides a simple but efficient way to make interactions between different hierarchical levels. Since iteration process will greatly increase the number of layers, we employ the residual network to improve the performance. In addition, we also introduce dilated k-nearest neighbors and multi-scale grouping to increase the receptive field. The experiments on both Vaihingen 3D dataset and Dayton Annotated LiDAR Earth Scan (DALES) dataset demonstrate the effectiveness of the proposed method in point cloud classification. Numéro de notice : A2021-867 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104932 Date de publication en ligne : 04/09/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104932 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99098 [article]