A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds
Individual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint n...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/11/2545 |
| _version_ | 1797491901982572544 |
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| author | Zhenyang Hui Zhaochen Cai Bo Liu Dajun Li Hua Liu Zhuoxuan Li |
| author_facet | Zhenyang Hui Zhaochen Cai Bo Liu Dajun Li Hua Liu Zhuoxuan Li |
| author_sort | Zhenyang Hui |
| collection | DOAJ |
| description | Individual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint neighboring growing method to segment wood points into object primitives. Subsequently, local object primitives were optimized to alleviate the computation burden. To build the topology relation among branches, branches were separated based on spatial connectivity analysis. And then the nodes corresponding to each object primitive were adopted to construct the graph structure of the tree. Furthermore, each object primitive was fitted as a cylinder. To revise the local abnormal cylinder, a self-adaptive optimization method based on the constructed graph structure was proposed. Finally, the constructed tree model was further optimized globally based on prior knowledge. Twenty-nine field datasets obtained from three forest sites were adopted to evaluate the performance of the proposed method. The experimental results show that the proposed method can achieve satisfying individual tree modeling accuracy. The mean volume deviation of the proposed method is 1.427 m<sup>3</sup>. In the comparison with two other famous tree modeling methods, the proposed method can achieve the best individual tree modeling result no matter which accuracy indicator is selected. |
| first_indexed | 2024-03-10T00:55:56Z |
| format | Article |
| id | doaj.art-96827441ad71441fb29a8cf8af1fa425 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:55:56Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-96827441ad71441fb29a8cf8af1fa4252023-11-23T14:43:27ZengMDPI AGRemote Sensing2072-42922022-05-011411254510.3390/rs14112545A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point CloudsZhenyang Hui0Zhaochen Cai1Bo Liu2Dajun Li3Hua Liu4Zhuoxuan Li5Faculty of Geomatics, East China University of Technology, Nanchang 330013, ChinaFaculty of Geomatics, East China University of Technology, Nanchang 330013, ChinaFaculty of Geomatics, East China University of Technology, Nanchang 330013, ChinaFaculty of Geomatics, East China University of Technology, Nanchang 330013, ChinaFaculty of Geomatics, East China University of Technology, Nanchang 330013, ChinaFaculty of Geomatics, East China University of Technology, Nanchang 330013, ChinaIndividual tree modeling for terrestrial LiDAR point clouds always involves heavy computation burden and low accuracy toward a complex tree structure. To solve these problems, this paper proposed a self-adaptive optimization individual tree modeling method. In this paper, we first proposed a joint neighboring growing method to segment wood points into object primitives. Subsequently, local object primitives were optimized to alleviate the computation burden. To build the topology relation among branches, branches were separated based on spatial connectivity analysis. And then the nodes corresponding to each object primitive were adopted to construct the graph structure of the tree. Furthermore, each object primitive was fitted as a cylinder. To revise the local abnormal cylinder, a self-adaptive optimization method based on the constructed graph structure was proposed. Finally, the constructed tree model was further optimized globally based on prior knowledge. Twenty-nine field datasets obtained from three forest sites were adopted to evaluate the performance of the proposed method. The experimental results show that the proposed method can achieve satisfying individual tree modeling accuracy. The mean volume deviation of the proposed method is 1.427 m<sup>3</sup>. In the comparison with two other famous tree modeling methods, the proposed method can achieve the best individual tree modeling result no matter which accuracy indicator is selected.https://www.mdpi.com/2072-4292/14/11/2545individual tree modelingterrestrial LiDARobject primitivejoint neighboring growingglobal optimization |
| spellingShingle | Zhenyang Hui Zhaochen Cai Bo Liu Dajun Li Hua Liu Zhuoxuan Li A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds Remote Sensing individual tree modeling terrestrial LiDAR object primitive joint neighboring growing global optimization |
| title | A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds |
| title_full | A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds |
| title_fullStr | A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds |
| title_full_unstemmed | A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds |
| title_short | A Self-Adaptive Optimization Individual Tree Modeling Method for Terrestrial LiDAR Point Clouds |
| title_sort | self adaptive optimization individual tree modeling method for terrestrial lidar point clouds |
| topic | individual tree modeling terrestrial LiDAR object primitive joint neighboring growing global optimization |
| url | https://www.mdpi.com/2072-4292/14/11/2545 |
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