NAOC Open IR
Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation
Hui, Zhenyang1,2; Jin, Shuanggen2,3; Li, Dajun1; Ziggah, Yao Yevenyo4; Liu, Bo1
2021
Source PublicationREMOTE SENSING
Volume13Issue:2Pages:30
AbstractIndividual tree extraction is an important process for forest resource surveying and monitoring. To obtain more accurate individual tree extraction results, this paper proposed an individual tree extraction method based on transfer learning and Gaussian mixture model separation. In this study, transfer learning is first adopted in classifying trunk points, which can be used as clustering centers for tree initial segmentation. Subsequently, principal component analysis (PCA) transformation and kernel density estimation are proposed to determine the number of mixed components in the initial segmentation. Based on the number of mixed components, the Gaussian mixture model separation is proposed to separate canopies for each individual tree. Finally, the trunk stems corresponding to each canopy are extracted based on the vertical continuity principle. Six tree plots with different forest environments were used to test the performance of the proposed method. Experimental results show that the proposed method can achieve 87.68% average correctness, which is much higher than that of other two classical methods. In terms of completeness and mean accuracy, the proposed method also outperforms the other two methods.
Keywordindividual tree extraction LiDAR point clouds transfer learning kernel density estimation Gaussian mixture model
Funding OrganizationChina Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; 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 ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; 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 ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; 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 ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; 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 ; 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
DOI10.3390/rs13020223
Language英语
Funding ProjectChina Post-Doctoral Science Foundation[2019M661858] ; National Natural Science Foundation of China (NSF)[41801325] ; Natural Science Foundation of Jiangxi Province[20192BAB217010] ; 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 OrganizationChina Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; 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 ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; 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 ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; 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 ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; 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 ; 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 AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing
WOS IDWOS:000611559000001
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/79827
Collection中国科学院国家天文台
Corresponding AuthorLi, Dajun
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 999064, Ghana
Recommended Citation
GB/T 7714
Hui, Zhenyang,Jin, Shuanggen,Li, Dajun,et al. Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation[J]. REMOTE SENSING,2021,13(2):30.
APA Hui, Zhenyang,Jin, Shuanggen,Li, Dajun,Ziggah, Yao Yevenyo,&Liu, Bo.(2021).Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation.REMOTE SENSING,13(2),30.
MLA Hui, Zhenyang,et al."Individual Tree Extraction from Terrestrial LiDAR Point Clouds Based on Transfer Learning and Gaussian Mixture Model Separation".REMOTE SENSING 13.2(2021):30.
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