Robust Interpolation of DEMs From Lidar-Derived Elevation Data
文献类型：期刊论文
| 作者 | Chen, Chuanfa1; Li, Yanyan2; Zhao, Na3; Yan, Changqing1 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 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. |
| WOS关键词 | LASER-SCANNING DATA ; MULTIQUADRIC METHOD ; OUTLIER DETECTION ; SAMPLING DENSITY ; DATA SETS ; MODELS ; REGRESSION ; ALGORITHMS ; EXTRACTION ; MORPHOLOGY |
| 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研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000424627500036 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 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 |
| 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. |
入库方式： OAI收割
来源：地理科学与资源研究所
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