Automatic DTM extraction from airborne LiDAR based on expectation-maximization
文献类型：期刊论文
| 作者 | Hui, Zhenyang1,5; Li, Dajun1; Jin, Shuanggen2 ; Ziggah, Yao Yevenyo3; Wang, Leyang1; Hu, Youjian4
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| 刊名 | OPTICS AND LASER TECHNOLOGY
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| 出版日期 | 2019-04-15 |
| 卷号 | 112页码:43-55 |
| 关键词 | Airborne LiDAR Point clouds Filtering Expectation-maximization Least-squares fitting |
| ISSN号 | 0030-3992 |
| DOI | 10.1016/j.optlastec.2018.10.051 |
| 英文摘要 | Filtering of ground points is a key step for most applications of airborne LiDAR point clouds. Although many filtering algorithms have been proposed in recent years, most of them suffer from parameter setting or thresholds fine-tuning. This is most often time-consuming and reduces the degree of automation of the applied algorithm. To overcome such problems, this paper proposes a threshold-free filtering algorithm based on expectation-maximization (EM). The filter is developed based on the assumption that point clouds are seen as a mixture of Gaussian models. Thus, the separation of ground points and non-ground points from point clouds is partitioning of the point clouds by a mixed Gaussian model that is used for screening ground points. EM is applied to realize the separation, which calculates the maximum likelihood estimates of the mixture parameters. Using the estimated parameters, the likelihoods of each point belonging to ground or non-ground are computed. Noticeably, point clouds are labeled as the component with a larger likelihood. The proposed method has been tested using the standard filtering datasets provided by the ISPRS. Experimental results showed that the proposed method performed the best in comparison with the classic progressive triangulated irregular network densification (PTD) and segment-based PTD methods in terms of omission error. The average omission error of the proposed method was 52.81% and 16.78% lower than the classic PTD method and the segment-based PTD method, respectively. Moreover, the proposed method was able to reduce its average total error by 31.95% compared to the classic PTD method. |
| 资助项目 | National Science Foundation (NSF), China[41801325] ; National Science Foundation (NSF), China[41861052] ; National Science Foundation (NSF), China[41874001] ; Education Department of Jiangxi Province, China[GJJ170449] ; Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology, China[DLLJ201806] ; East China University of Technology Ph.D. Project, China[DHBK2017155] |
| WOS研究方向 | Optics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000458941800007 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://119.78.226.72/handle/331011/31917]  |
| 专题 | 中国科学院上海天文台 |
| 通讯作者 | Hui, Zhenyang; Li, Dajun |
| 作者单位 | 1.East China Univ Technol, Fac Geomat, Nanchang 330013, Jiangxi, Peoples R China 2.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China 3.Univ Mines & Technol, Fac Mineral Resources Technol, Tarkwa, Ghana 4.China Univ Geosci, Fac Informat Engn, Wuhan 430074, Hubei, Peoples R China 5.East China Univ Technol, Key Lab Digital Land & Resources Jiangxi Prov, Nanchang 330013, Jiangxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hui, Zhenyang,Li, Dajun,Jin, Shuanggen,et al. Automatic DTM extraction from airborne LiDAR based on expectation-maximization[J]. OPTICS AND LASER TECHNOLOGY,2019,112:43-55. |
| APA | Hui, Zhenyang,Li, Dajun,Jin, Shuanggen,Ziggah, Yao Yevenyo,Wang, Leyang,&Hu, Youjian.(2019).Automatic DTM extraction from airborne LiDAR based on expectation-maximization.OPTICS AND LASER TECHNOLOGY,112,43-55. |
| MLA | Hui, Zhenyang,et al."Automatic DTM extraction from airborne LiDAR based on expectation-maximization".OPTICS AND LASER TECHNOLOGY 112(2019):43-55. |
入库方式： OAI收割
来源：上海天文台
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