A Random Features-Based Method for Interpolating Digital Terrain Models with High Efficiency

doi:10.1007/s11004-019-09801-z

	A Random Features-Based Method for Interpolating Digital Terrain Models with High Efficiency
	Chen, Chuanfa 1,2; Li, Yanyan 2; Yan, Changqing 3
刊名	MATHEMATICAL GEOSCIENCES
	2020-02-01
卷号	52 期号:2 页码:191-212
关键词	Interpolation Lidar Digital terrain model Accuracy
ISSN号	1874-8961
DOI	10.1007/s11004-019-09801-z
通讯作者	Chen, Chuanfa(chencf@lreis.ac.cn)
英文摘要	Airborne light detection and ranging (lidar) is becoming a widely adopted technique for capturing elevation data, which are mainly used for creating digital terrain models (DTMs). However, the large size of lidar datasets poses a serious computational challenge to the promising radial basis function (RBF) interpolation method. In this work, to reduce the huge computational cost and improve the interpolation accuracy, random Fourier features are first introduced to approximate the Gaussian kernel of RBFs in feature space, then a random features-based weighted RBF interpolation method is developed. Based on randomized Fourier features, the nonlinear kernel-based training and evaluation of the RBF method is transformed into simple linear operations in feature space, and with the help of weighted ridge regression, the negative effect of the non-Gaussian distribution of lidar datasets on DTM production is reduced. In other words, the combination of randomized Fourier features and weighted ridge regression improves the efficiency and accuracy of the RBF interpolation method. Experiments on simulated datasets indicate that the proposed method performs better than the classical or random features-based RBF method for dealing with non-Gaussian distributed samples, with the former being slightly less accurate than the iterative RBF method due to the low-dimensional random features. However, the computational cost of the new method is much lower compared with the classical or iterative RBFs. Interpolation of airborne lidar-derived points demonstrates that the new method has a computational cost similar to the inverse distance weighting and triangulated irregular network (TIN) approaches, and is significantly faster than the ordinary kriging (OK) or thin plate spline (TPS) methods. Quantitatively, for interpolation of 644,433 points, the proposed method is approximately 833 and 21 times faster than OK and TPS, respectively. Moreover, the new method avoids the surface discontinuity artifacts presented by the OK, TPS, and TIN methods.
资助项目	National Natural Science Foundation of China[41804001] ; National Natural Science Foundation of China[41371367] ; Shandong Provincial Natural Science Foundation, China ; State Key Laboratory of Resources and Environmental Information System ; SDUST Research Fund
WOS关键词	RADIAL BASIS FUNCTIONS ; ELEVATION MODELS ; SAMPLING DENSITY ; SCATTERED DATA ; ALGORITHM ; ACCURACY ; GMRES
WOS研究方向	Geology ; Mathematics
语种	英语
出版者	SPRINGER HEIDELBERG
WOS记录号	WOS:000512080000003
资助机构	National Natural Science Foundation of China ; Shandong Provincial Natural Science Foundation, China ; State Key Laboratory of Resources and Environmental Information System ; SDUST Research Fund
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/132370]
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.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 2.Shandong Univ Sci & Technol, Coll Geomat, Qingdao, Peoples R China 3.Shandong Univ Sci & Technol, Dept Informat Engn, Tai An, Shandong, Peoples R China
推荐引用方式 GB/T 7714	Chen, Chuanfa,Li, Yanyan,Yan, Changqing. A Random Features-Based Method for Interpolating Digital Terrain Models with High Efficiency[J]. MATHEMATICAL GEOSCIENCES,2020,52(2):191-212.
APA	Chen, Chuanfa,Li, Yanyan,&Yan, Changqing.(2020).A Random Features-Based Method for Interpolating Digital Terrain Models with High Efficiency.MATHEMATICAL GEOSCIENCES,52(2),191-212.
MLA	Chen, Chuanfa,et al."A Random Features-Based Method for Interpolating Digital Terrain Models with High Efficiency".MATHEMATICAL GEOSCIENCES 52.2(2020):191-212.