NAOC Open IR
A mean shift segmentation morphological filter for airborne LiDAR DTM extraction under forest canopy
Hui, Zhenyang1,2,3; Jin, Shuanggen2,4; Xia, Yuanping1; Nie, Yunju1; Xie, Xiaowei1; Li, Na1
2021-04-01
Source PublicationOPTICS AND LASER TECHNOLOGY
ISSN0030-3992
Volume136Pages:15
AbstractIn recent years, many airborne point clouds filtering methods have been developed. However, it is still challenging for distinguishing ground and non-ground points in forested areas due to the rugged terrains, dense vegetation canopy and low-level penetration of laser pulses. To derive satisfactory filtering results, this paper proposed a mean shift segmentation morphological filter. In this method, the mean shift segmentation is used for acquiring object primitives to determine filtering window sizes automatically. The point clouds detrending is proposed for improving the adaptability towards sloped terrains. A point cloud shifting in x and y directions technique is developed to acquire more ground seeds for generating a more accurate trending surface. Finally, the filtered ground points by the progressive morphological filter are recovered by adopting the surface-based filtering strategy. The proposed method is tested and validated using 14 samples with different forested environments. Experimental results show that the proposed method can achieve the average total error of 1.11%. The kappa coefficients of all these 14 samples are larger than 90% and the average kappa coefficient is 96.43%. The average mot mean square error (RMSE) of the proposed method is 0.63. All these indicators are the best when compared to some other famous filtering methods.
KeywordAirborne LiDAR Filtering Mean shift Morphological filter Forested environment
Funding OrganizationNational 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; East China University of Technology ; East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; East China University of Technology ; East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; East China University of Technology ; East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; East China University of Technology ; East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province
DOI10.1016/j.optlastec.2020.106728
WOS KeywordPOINT CLOUDS ; ALGORITHM ; CLASSIFICATION ; DENSIFICATION ; GENERATION ; AREAS
Language英语
Funding ProjectNational Natural Science Foundation of China (NSF)[41801325] ; National Natural Science Foundation of China (NSF)[41962018] ; Natural Science Foundation of Jiangxi Province[20192BAB217010] ; China Post-Doctoral Science Foundation[2019M661858] ; Education Department of Jiangxi Province[GJJ170449] ; East China University of Technology[DLLJ201806] ; East China University of Technology Ph.D. Project[DHBK2017155] ; Key Laboratory for Digital Land and Resources of Jiangxi Province
Funding OrganizationNational 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; East China University of Technology ; East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; East China University of Technology ; East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; East China University of Technology ; East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; 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 ; China Post-Doctoral Science Foundation ; China Post-Doctoral Science Foundation ; Education Department of Jiangxi Province ; Education Department of Jiangxi Province ; East China University of Technology ; East China University of Technology ; East China University of Technology Ph.D. Project ; East China University of Technology Ph.D. Project ; Key Laboratory for Digital Land and Resources of Jiangxi Province ; Key Laboratory for Digital Land and Resources of Jiangxi Province
WOS Research AreaOptics ; Physics
WOS SubjectOptics ; Physics, Applied
WOS IDWOS:000607111600006
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/80023
Collection中国科学院国家天文台
Corresponding AuthorXia, Yuanping
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.Nanjing Longyuan Microelect Technol Co Ltd, Nanjing 211106, Peoples R China
4.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China
Recommended Citation
GB/T 7714
Hui, Zhenyang,Jin, Shuanggen,Xia, Yuanping,et al. A mean shift segmentation morphological filter for airborne LiDAR DTM extraction under forest canopy[J]. OPTICS AND LASER TECHNOLOGY,2021,136:15.
APA Hui, Zhenyang,Jin, Shuanggen,Xia, Yuanping,Nie, Yunju,Xie, Xiaowei,&Li, Na.(2021).A mean shift segmentation morphological filter for airborne LiDAR DTM extraction under forest canopy.OPTICS AND LASER TECHNOLOGY,136,15.
MLA Hui, Zhenyang,et al."A mean shift segmentation morphological filter for airborne LiDAR DTM extraction under forest canopy".OPTICS AND LASER TECHNOLOGY 136(2021):15.
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