NAOC Open IR
Wood and leaf separation from terrestrial LiDAR point clouds based on mode points evolution
Hui, Zhenyang1,2; Jin, Shuanggen2,3; Xia, Yuanping1; Wang, Leyang1; Ziggah, Yao Yevenyo4; Cheng, Penggen1
2021-08-01
Source PublicationISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
ISSN0924-2716
Volume178Pages:219-239
AbstractTo improve the accuracy of wood and leaf points classification for individual tree, this paper proposed a separation method based on mode points evolution from terrestrial LiDAR point clouds. In the proposed method, the Mean Shift method was used to first acquire the mode points, which were then adopted as nodes to build a network graph for the individual tree. By path retracing and calculating the visiting frequency of each node, the wood seed nodes were detected. To obtain more wood nodes, the wood seed nodes were evolved based on three constraints, namely the shortest path length of the evolved nodes to the base node should be smaller, the evolved nodes should not belong to the leaf nodes that have been detected by path retracing and the verticality of the evolved nodes should be similar as the wood seed nodes. After wood nodes evolution, the segments corresponding to each wood seed node were merged together to obtain the final wood points. The proposed method has been evaluated using nine tree samples with seven different tree species. Experimental results showed that the proposed method can achieve an average wood and leaf classification accuracy of 0.892. The average F1 score for wood was 0.871, while the average F1 score for leaf was 0.900. Compared to two other famous wood and leaf classification methods, the proposed method can achieve better classification results.
KeywordTerrestrial LiDAR point clouds Wood and leaf separation Mean shift Mode points Evolution
Funding OrganizationNational Natural Science Foundation of China (NSF) ; National Natural Science Foundation of China (NSF) ; Natural Science Foundation of Jiangxi Province ; Natural Science Foundation of Jiangxi Province ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; National Natural Science Foundation of China (NSF) ; National Natural Science Foundation of China (NSF) ; Natural Science Foundation of Jiangxi Province ; Natural Science Foundation of Jiangxi Province ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; National Natural Science Foundation of China (NSF) ; National Natural Science Foundation of China (NSF) ; Natural Science Foundation of Jiangxi Province ; Natural Science Foundation of Jiangxi Province ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; National Natural Science Foundation of China (NSF) ; National Natural Science Foundation of China (NSF) ; Natural Science Foundation of Jiangxi Province ; Natural Science Foundation of Jiangxi Province ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project
DOI10.1016/j.isprsjprs.2021.06.012
WOS KeywordTREE SPECIES CLASSIFICATION ; AREA INDEX ; FOREST ; BIOMASS ; STEM ; RECONSTRUCTION ; ALGORITHM ; HEIGHT
Language英语
Funding ProjectNational Natural Science Foundation of China (NSF)[41801325] ; National Natural Science Foundation of China (NSF)[41861052] ; National Natural Science Foundation of China (NSF)[41874001] ; Natural Science Foundation of Jiangxi Province[20192BAB217010] ; China Post-Doctoral Science Foundation[2019M661858] ; Education Department of Jiangxi Province[GJJ170449] ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology[DLLJ201806] ; East China University of Technology Ph.D. Project[DHBK2017155]
Funding OrganizationNational Natural Science Foundation of China (NSF) ; National Natural Science Foundation of China (NSF) ; Natural Science Foundation of Jiangxi Province ; Natural Science Foundation of Jiangxi Province ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; National Natural Science Foundation of China (NSF) ; National Natural Science Foundation of China (NSF) ; Natural Science Foundation of Jiangxi Province ; Natural Science Foundation of Jiangxi Province ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; National Natural Science Foundation of China (NSF) ; National Natural Science Foundation of China (NSF) ; Natural Science Foundation of Jiangxi Province ; Natural Science Foundation of Jiangxi Province ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; National Natural Science Foundation of China (NSF) ; National Natural Science Foundation of China (NSF) ; Natural Science Foundation of Jiangxi Province ; Natural Science Foundation of Jiangxi Province ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project
WOS Research AreaPhysical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000669954900016
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/76980
Collection中国科学院国家天文台
Corresponding AuthorCheng, Penggen
Affiliation1.East China Univ Technol, Fac Geomat, Nanchang 330013, Jiangxi, Peoples R China
2.Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China
3.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China
4.Univ Mines & Technol, Fac Mineral Resources Technol, Tarkwa, Ghana
Recommended Citation
GB/T 7714
Hui, Zhenyang,Jin, Shuanggen,Xia, Yuanping,et al. Wood and leaf separation from terrestrial LiDAR point clouds based on mode points evolution[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2021,178:219-239.
APA Hui, Zhenyang,Jin, Shuanggen,Xia, Yuanping,Wang, Leyang,Ziggah, Yao Yevenyo,&Cheng, Penggen.(2021).Wood and leaf separation from terrestrial LiDAR point clouds based on mode points evolution.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,178,219-239.
MLA Hui, Zhenyang,et al."Wood and leaf separation from terrestrial LiDAR point clouds based on mode points evolution".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 178(2021):219-239.
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