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Scalable individual tree delineation in 3D point clouds / Jinhu Wang in Photogrammetric record, vol 33 n° 163 (September 2018)  

Public [article]  inPhotogrammetric record > vol 33 n° 163 (September 2018) . - pp 315 - 340 Titre : Scalable individual tree delineation in 3D point clouds Type de document : Article/Communication Auteurs : Jinhu Wang, Auteur ; Roderik Lindenbergh, Auteur ; Massimo Menenti, Auteur Année de publication : 2018 Article en page(s) : pp 315 - 340 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement [Termes IGN] arbre (flore) [Termes IGN] délimitation [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] inventaire de la végétation [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lasergrammétrie [Termes IGN] semis de points [Vedettes matières IGN] Inventaire forestier Résumé : (Auteur) Manually monitoring and documenting trees is labour intensive. Lidar provides a possible solution for automatic tree‐inventory generation. Existing approaches for segmenting trees from original point cloud data lack scalable and efficient methods that separate individual trees sampled by different laser‐scanning systems with sufficient quality under all circumstances. In this study a new algorithm for efficient individual tree delineation from lidar point clouds is presented and validated. The proposed algorithm first resamples the points using cuboid (modified voxel) cells. Consecutively connected cells are accumulated by vertically traversing cell layers. Trees in close proximity are identified, based on a novel cell‐adjacency analysis. The scalable performance of this algorithm is validated on airborne, mobile and terrestrial laser‐scanning point clouds. Validation against ground truth demonstrates an improvement from 89% to 94% relative to a state‐of‐the‐art method while computation time is similar. Numéro de notice : A2018-619 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12247 Date de publication en ligne : 16/07/2018 En ligne : https://doi.org/10.1111/phor.12247 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92863 [article]

Extraction of building roof planes with stratified random sample consensus / André C. Carrilho in Photogrammetric record, vol 33 n° 163 (September 2018)  

Public [article]  inPhotogrammetric record > vol 33 n° 163 (September 2018) . - pp 363 - 380 Titre : Extraction of building roof planes with stratified random sample consensus Type de document : Article/Communication Auteurs : André C. Carrilho, Auteur ; Mauricio Galo, Auteur Année de publication : 2018 Article en page(s) : pp 363 - 380 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] morphologie mathématique [Termes IGN] Ransac (algorithme) [Termes IGN] semis de points [Termes IGN] toit [Termes IGN] variance Résumé : (Auteur) This paper describes a consensus‐set estimation for building roof‐plane detection using a stratified random sample consensus (sRANSAC) algorithm applied to point clouds acquired by laser scanning systems. The main idea is to use one initial classification to generate consensus‐set candidates to optimise the sampling mechanism compared to the original RANSAC. The initial classification is performed using mathematical morphology to filter ground returns and estimate local variance information to detect potential planar regions. Thus, the algorithm can prioritise points within planar segments and the number of iterations can be estimated dynamically from available data. The results based on experiments using five different lidar datasets indicate that the proposed method reduces the number of computations for building roof‐plane detection and also improves accuracy compared to RANSAC. Numéro de notice : A2018-620 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12254 Date de publication en ligne : 21/09/2018 En ligne : https://doi.org/10.1111/phor.12254 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92864 [article]