Robust Interpolation of DEMs From Lidar-Derived Elevation Data

doi:10.1109/TGRS.2017.2758795

	Robust Interpolation of DEMs From Lidar-Derived Elevation Data
	Chen, Chuanfa 1; Li, Yanyan 2; Zhao, Na 3; Yan, Changqing 1
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2018-02-01
卷号	56 期号:2 页码:1059-1068
关键词	Interpolation noise robustness surface fitting
ISSN号	0196-2892
DOI	10.1109/TGRS.2017.2758795
通讯作者	Chen, Chuanfa(chencf@lreis.ac.cn)
英文摘要	Light detection and ranging (lidar)-derived elevation data are commonly subjected to outliers due to the boundaries of occlusions, physical imperfections of sensors, and surface reflectance. Outliers have a serious negative effect on the accuracy of digital elevation models (DEMs). To decrease the impact of outliers on DEM construction, we propose a robust interpolation algorithm of multiquadric (MQ) based on a regularized least absolute deviation (LAD) technique. The objective function of the proposed method includes a regularization-based smoothing term and an LAD-based fitting term, respectively, used to smooth noisy samples and resist the influence of outliers. To solve the objective function of the proposed method, we develop a simple scheme based on the split-Bregman iteration algorithm. Results from simulated data sets indicate that when sample points are noisy or contaminated by outliers, the proposed method is more accurate than the classical MQ and two recently developed robust algorithms of MQ for surface modeling. Real-world examples of interpolating 1 private and 11 publicly available airborne lidarderived data sets demonstrate that the proposed method averagely produces better results than two promising interpolation methods including regularized spline with tension (RST) and gridded data-based robust thin plate spline (RTPS). Specifically, the image of RTPS is too smooth to retain terrain details. Although RST can keep subtle terrain features, it is distorted by some misclassified object points (i.e., pseudooutliers). The proposed method obtains a good tradeoff between resisting the effect of outliers and preserving terrain features. Overall, the proposed method can be considered as an alternative for interpolating lidar-derived data sets potentially including outliers.
资助项目	National Natural Science Foundation of China[41371367] ; SDUST Research Fund ; Joint Innovative Center for Safe and Effective Mining Technology and Equipment of Coal Resources ; State Key Laboratory of Resources and Environmental Information System
WOS关键词	LASER-SCANNING DATA ; MULTIQUADRIC METHOD ; OUTLIER DETECTION ; SAMPLING DENSITY ; DATA SETS ; MODELS ; REGRESSION ; ALGORITHMS ; EXTRACTION ; MORPHOLOGY
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000424627500036
资助机构	National Natural Science Foundation of China ; SDUST Research Fund ; Joint Innovative Center for Safe and Effective Mining Technology and Equipment of Coal Resources ; State Key Laboratory of Resources and Environmental Information System
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/57144]
专题	中国科学院地理科学与资源研究所
通讯作者	Chen, Chuanfa
作者单位	1.Shandong Univ Sci & Technol, Shandong Prov & Minist Sci & Technol, State Key Lab Min Disaster Prevent & Control, Qingdao 266590, Peoples R China 2.Wuhan Univ, Sch Geodesy & Geomat, Wuhan 430079, Hubei, Peoples R China 3.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	Chen, Chuanfa,Li, Yanyan,Zhao, Na,et al. Robust Interpolation of DEMs From Lidar-Derived Elevation Data[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2018,56(2):1059-1068.
APA	Chen, Chuanfa,Li, Yanyan,Zhao, Na,&Yan, Changqing.(2018).Robust Interpolation of DEMs From Lidar-Derived Elevation Data.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,56(2),1059-1068.
MLA	Chen, Chuanfa,et al."Robust Interpolation of DEMs From Lidar-Derived Elevation Data".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.2(2018):1059-1068.