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

doi:10.3390/rs11030342

	Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification
	Xu, Zongxia 1,2; Zhang, Zhenxin 1,2,3; Zhong, Ruofei 1,2; Chen, Dong 4; Sun, Taochun 1,2; Deng, Xin 5; Li, Zhen 1,2; Qin, Cheng-Zhi 6
刊名	REMOTE SENSING
	2019-02-01
卷号	11 期号:3 页码:19
关键词	ALS point cloud content-sensitive multilevel point clusters hierarchical classification framework
ISSN号	2072-4292
DOI	10.3390/rs11030342
通讯作者	Zhang, Zhenxin(zhangzhx@cnu.edu.cn) ; Zhong, Ruofei(zrfsss@163.com)
英文摘要	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.
资助项目	National Natural Science Foundation of China[41701533] ; State Key Laboratory of Resources and Environmental Information System ; Open Fund of the State Key Laboratory of Remote Sensing Science[OFSLRSS201818]
WOS关键词	FEATURE-EXTRACTION ; LIDAR DATA ; MULTISCALE ; FEATURES ; OBJECTS
WOS研究方向	Remote Sensing
语种	英语
出版者	MDPI
WOS记录号	WOS:000459944400130
资助机构	National Natural Science Foundation of China ; State Key Laboratory of Resources and Environmental Information System ; Open Fund of the State Key Laboratory of Remote Sensing Science
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/49327]
专题	中国科学院地理科学与资源研究所
通讯作者	Zhang, Zhenxin; Zhong, Ruofei
作者单位	1.Capital Normal Univ, Beijing Adv Innovat Ctr Imaging Theory & Technol, Beijing 100048, Peoples R China 2.Capital Normal Univ, Key Lab 3D Informat Acquisit & Applicat, Beijing 100048, Peoples R China 3.Chinese Acad Surveying & Mapping, Beijing 100830, Peoples R China 4.Nanjing Forestry Univ, Coll Civil Engn, Nanjing 210037, Jiangsu, Peoples R China 5.Chinese Soc Urban Studies, Beijing 100835, Peoples R China 6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式 GB/T 7714	Xu, Zongxia,Zhang, Zhenxin,Zhong, Ruofei,et al. Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification[J]. REMOTE SENSING,2019,11(3):19.
APA	Xu, Zongxia.,Zhang, Zhenxin.,Zhong, Ruofei.,Chen, Dong.,Sun, Taochun.,...&Qin, Cheng-Zhi.(2019).Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification.REMOTE SENSING,11(3),19.
MLA	Xu, Zongxia,et al."Content-Sensitive Multilevel Point Cluster Construction for ALS Point Cloud Classification".REMOTE SENSING 11.3(2019):19.