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Integrating topographic knowledge into point cloud simplification for terrain modelling / Jun Chen in International journal of geographical information science IJGIS, vol 37 n° 5 (May 2023)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 37 n° 5 (May 2023) . - pp 988 - 1008 Titre : Integrating topographic knowledge into point cloud simplification for terrain modelling Type de document : Article/Communication Auteurs : Jun Chen, Auteur ; Liyang Xiong, Auteur ; Bowen Yin, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 988 - 1008 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données topographiques [Termes IGN] lissage de données [Termes IGN] modèle numérique de surface [Termes IGN] semis de points [Termes IGN] Triangulated Irregular Network Résumé : (auteur) Terrain models are widely used to depict the shape of the Earth’s surface. With the development of photogrammetric methods, point cloud data have become one of the most popular data sources for terrain modelling. However, the obtained point clouds are of high density, which often increases redundancy rather than improving accuracy. Therefore, point cloud simplification should be a core component of terrain modelling. This paper proposes a point cloud simplification method by integrating topographic knowledge into terrain modelling (TKPCS). The method contains two steps: (1) topographic knowledge recognition and construction and (2) point cloud simplification using this topographic knowledge for terrain modelling. The proposed approach is benchmarked against improved versions of existing methods to validate its capability and accuracy in digital elevation model construction and terrain derivative extraction. The results show that the simplified points of the TKPCS method can generate finer resolution terrain models with higher accuracy and greater information entropy. The good performance of the TKPCS method is also stable at different scales. This work endeavours to transform perceptive topographic knowledge into a process of point cloud simplification and can benefit future research related to terrain modelling. Numéro de notice : A2023-204 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658816.2023.2180801 Date de publication en ligne : 28/02/2023 En ligne : https://doi.org/10.1080/13658816.2023.2180801 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103138 [article]