A Random Features-Based Method for Interpolating Digital Terrain Models with High Efficiency
文献类型：期刊论文
| 作者 | Chen, Chuanfa1,3; Li, Yanyan3; Yan, Changqing2 |
| 刊名 | MATHEMATICAL GEOSCIENCES
 |
| 出版日期 | 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. |
| WOS关键词 | RADIAL BASIS FUNCTIONS ; ELEVATION MODELS ; SAMPLING DENSITY ; SCATTERED DATA ; ALGORITHM ; ACCURACY ; GMRES |
| 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研究方向 | Geology ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000512080000003 |
| 出版者 | SPRINGER HEIDELBERG |
| 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 |
| 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, Dept Informat Engn, Tai An, Shandong, Peoples R China 3.Shandong Univ Sci & Technol, Coll Geomat, Qingdao, 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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