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Hierarchical extraction of urban objects from mobile laser scanning data / Bisheng Yang in ISPRS Journal of photogrammetry and remote sensing, vol 99 (January 2015)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 99 (January 2015) . - pp 45 - 57 Titre : Hierarchical extraction of urban objects from mobile laser scanning data Type de document : Article/Communication Auteurs : Bisheng Yang, Auteur ; Zhen Dong, Auteur ; Gang Zhao, Auteur ; Wenxia Dai, Auteur Année de publication : 2015 Article en page(s) : pp 45 - 57 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection d'objet [Termes IGN] extraction automatique [Termes IGN] levé urbain [Termes IGN] semis de points [Termes IGN] Station laser ultra-mobile [Termes IGN] traitement de données [Termes IGN] voxel Résumé : (Auteur) Point clouds collected in urban scenes contain a huge number of points (e.g., billions), numerous objects with significant size variability, complex and incomplete structures, and variable point densities, raising great challenges for the automated extraction of urban objects in the field of photogrammetry, computer vision, and robotics. This paper addresses these challenges by proposing an automated method to extract urban objects robustly and efficiently. The proposed method generates multi-scale supervoxels from 3D point clouds using the point attributes (e.g., colors, intensities) and spatial distances between points, and then segments the supervoxels rather than individual points by combining graph based segmentation with multiple cues (e.g., principal direction, colors) of the supervoxels. The proposed method defines a set of rules for merging segments into meaningful units according to types of urban objects and forms the semantic knowledge of urban objects for the classification of objects. Finally, the proposed method extracts and classifies urban objects in a hierarchical order ranked by the saliency of the segments. Experiments show that the proposed method is efficient and robust for extracting buildings, streetlamps, trees, telegraph poles, traffic signs, cars, and enclosures from mobile laser scanning (MLS) point clouds, with an overall accuracy of 92.3%. Numéro de notice : A2014-635 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.10.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.10.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75061 [article]

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