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Article

Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification

1
Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing 100048, China
2
Key Lab of 3D Information Acquisition and Application, Capital Normal University, Beijing 100048, China
3
Chinese Academy of Surveying and Mapping, Beijing 100830, China
4
College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
5
Chinese Society for Urban Studies, Beijing 100835, China
6
State Key Laboratory of Resources and Environmental Information System, the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2019, 11(3), 342; https://doi.org/10.3390/rs11030342
Received: 13 December 2018 / Revised: 28 January 2019 / Accepted: 4 February 2019 / Published: 9 February 2019
(This article belongs to the Special Issue Future Trends and Applications for Airborne Laser Scanning)
Airborne laser scanning (ALS) point cloud classification is a challenge due to factors including complex scene structure, various densities, surface morphology, and the number of ground objects. A point cloud classification method is presented in this paper, based on content-sensitive multilevel objects (point clusters) in consideration of the density distribution of ground objects. The space projection method is first used to convert the three-dimensional point cloud into a two-dimensional (2D) image. The image is then mapped to the 2D manifold space, and restricted centroidal Voronoi tessellation is built for initial segmentation of content-sensitive point clusters. Thus, the segmentation results take the entity content (density distribution) into account, and the initial classification unit is adapted to the density of ground objects. The normalized cut is then used to segment the initial point clusters to construct content-sensitive multilevel point clusters. Following this, the point-based hierarchical features of each point cluster are extracted, and the multilevel point-cluster feature is constructed by sparse coding and latent Dirichlet allocation models. Finally, the hierarchical classification framework is created based on multilevel point-cluster features, and the AdaBoost classifiers in each level are trained. The recognition results of different levels are combined to effectively improve the classification accuracy of the ALS point cloud in the test process. Two scenes are used to experimentally test the method, and it is compared with three other state-of-the-art techniques. View Full-Text
Keywords: ALS point cloud; content-sensitive multilevel point clusters; hierarchical classification framework ALS point cloud; content-sensitive multilevel point clusters; hierarchical classification framework
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MDPI and ACS Style

Xu, Z.; Zhang, Z.; Zhong, R.; Chen, D.; Sun, T.; Deng, X.; Li, Z.; Qin, C.-Z. Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification. Remote Sens. 2019, 11, 342. https://doi.org/10.3390/rs11030342

AMA Style

Xu Z, Zhang Z, Zhong R, Chen D, Sun T, Deng X, Li Z, Qin C-Z. Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification. Remote Sensing. 2019; 11(3):342. https://doi.org/10.3390/rs11030342

Chicago/Turabian Style

Xu, Zongxia, Zhenxin Zhang, Ruofei Zhong, Dong Chen, Taochun Sun, Xin Deng, Zhen Li, and Cheng-Zhi Qin. 2019. "Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification" Remote Sensing 11, no. 3: 342. https://doi.org/10.3390/rs11030342

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