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Individual tree extraction from UAV lidar point clouds based on self-adaptive mean shift segmentation / Zhenyang Hui in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2021 (July 2021)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-1-2021 (July 2021) . - pp 25 - 30 Titre : Individual tree extraction from UAV lidar point clouds based on self-adaptive mean shift segmentation Type de document : Article/Communication Auteurs : Zhenyang Hui, Auteur ; N. Li, Auteur ; Y. Xia, Auteur ; Penggen Cheng, Auteur ; Y. He, Auteur Année de publication : 2021 Conférence : ISPRS 2021, Commission 1, XXIV ISPRS Congress, Imaging today foreseeing tomorrow 05/07/2021 09/07/2021 Nice on-line France OA Annals Commission 1 Article en page(s) : pp 25 - 30 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme de décalage moyen [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction d'arbres [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] segmentation [Termes IGN] semis de points Résumé : (auteur) Unman aerial vehicle (UAV) LiDAR has been widely used in the field of forestry. Individual tree extraction is a key step for forest inventory. Although many individual tree extraction methods have been proposed, the individual tree extraction accuracy is still low due to the complex forest environments. Moreover, many parameters in these methods generally need to be set. Thus, the degree of automation of the methods is generally low. To solve these problems, this paper proposed an automatic mean shift segmentation method, in which the kernel bandwidths can be calculated self-adaptively. Meanwhile, a hierarchy mean shift segmentation technique was proposed to extract individual tree gradually. A plot-level UAV LiDAR tree dataset was adopted for testing the performance of the proposed method. Experimental results showed that the proposed method can achieve better individual tree extraction result without any parameter setting. Compared with the traditional mean shift segmentation method, both the completeness and mean accuracy of the proposed method are higher. Numéro de notice : A2021-318 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-1-2021-25-2021 Date de publication en ligne : 17/06/2021 En ligne : https://doi.org/10.5194/isprs-annals-V-1-2021-25-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97950 [article]