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Object-based classification of terrestrial laser scanning point clouds for landslide monitoring / Andreas Mayr in Photogrammetric record, vol 32 n° 160 (December 2017)  

Public [article]  inPhotogrammetric record > vol 32 n° 160 (December 2017) . - pp 377 - 397 Titre : Object-based classification of terrestrial laser scanning point clouds for landslide monitoring Type de document : Article/Communication Auteurs : Andreas Mayr, Auteur ; Martin Rutzinger, Auteur ; Magnus Bremer, Auteur ; Sander J. Oude Elberink, Auteur ; Felix Stumpf, Auteur ; Clemens Geitner, Auteur Année de publication : 2017 Conférence : VGC 2016, 2nd virtual geoscience conference 22/09/2016 23/09/2016 Bergen Norvège Proceedings Wiley Article en page(s) : pp 377 - 397 Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage automatique [Termes IGN] classification orientée objet [Termes IGN] compréhension de l'image [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] effondrement de terrain [Termes IGN] relation topologique 3D [Termes IGN] semis de points [Termes IGN] surveillance géologique Résumé : (auteur) Terrestrial laser scanning (TLS) is often used to monitor landslides and other gravitational mass movements with high levels of geometric detail and accuracy. However, unstructured TLS point clouds lack semantic information, which is required to geomorphologically interpret the measured changes. Extracting meaningful objects in a complex and dynamic environment is challenging due to the objects' fuzziness in reality, as well as the variability and ambiguity of their patterns in a morphometric feature space. This work presents a point‐cloud‐based approach for classifying multitemporal scenes of a hillslope affected by shallow landslides. The 3D point clouds are segmented into morphologically homogeneous and spatially connected parts. These segments are classified into seven target classes (scarp, eroded area, deposit, rock outcrop and different classes of vegetation) in a two‐step procedure: a supervised classification step with a machine‐learning classifier using morphometric features, followed by a correction step based on topological rules. This improves the final object extraction considerably. Numéro de notice : A2017-899 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12215 Date de publication en ligne : 13/12/2017 En ligne : https://doi.org/10.1111/phor.12215 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89522 [article]