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Image classification-based ground filtering of point clouds extracted from UAV-based aerial photos / Volkan Yilmaz in Geocarto international, vol 33 n° 3 (March 2018)  

Public [article]  inGeocarto international > vol 33 n° 3 (March 2018) . - pp 310 - 320 Titre : Image classification-based ground filtering of point clouds extracted from UAV-based aerial photos Type de document : Article/Communication Auteurs : Volkan Yilmaz, Auteur ; Berkant Konakoglu, Auteur ; Cigdem Serifoglu, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 310 - 320 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de filtrage [Termes IGN] classification dirigée [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données localisées 3D [Termes IGN] drone [Termes IGN] image aérienne [Termes IGN] modèle numérique de surface [Termes IGN] semis de points [Termes IGN] Turquie Résumé : (Auteur) With the advent of unmanned aerial vehicles (UAVs) for mapping applications, it is possible to generate 3D dense point clouds using stereo images. This technology, however, has some disadvantages when compared to Light Detection and Ranging (LiDAR) system. Unlike LiDAR, digital cameras mounted on UAVs are incapable of viewing beneath the canopy, which leads to sparse points on the bare earth surface. In such cases, it is more challenging to remove points belonging to above-ground objects using ground filtering algorithms generated especially for LiDAR data. To tackle this problem, a methodology employing supervised image classification for filtering 3D point clouds is proposed in this study. A classified image is overlapped with the point cloud to determine the ground points to be used for digital elevation model (DEM) generation. Quantitative evaluation results showed that filtering the point cloud with this methodology has a good potential for high-resolution DEM generation. Numéro de notice : A2018-035 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1250825 En ligne : https://doi.org/10.1080/10106049.2016.1250825 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89213 [article]