[논문]Robust Interpolation of DEMs From Lidar-Derived Elevation Data

Chen, Chuanfa; Li, Yanyan; Zhao, Na; Yan, Changqing

doi:10.1109/TGRS.2017.2758795

Robust Interpolation of DEMs From Lidar-Derived Elevation Data

IEEE transactions on geoscience and remote sensing : a publication of the IEEE Geoscience and Remote Sensing Society, v.56 no.2, 2018년, pp.1059 - 1068

Chen, Chuanfa (State Key Laboratory of Mining Disaster Prevention and Control co-founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao, China) , Li, Yanyan (School of Geodesy and Geomatics, Wuhan University, Wuhan, China) , Zhao, Na (State Key Laboratory of Resources and Environment Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China) , Yan, Changqing (State Key Laboratory of Mining Disaster Prevention and Control co-founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao, China)

Abstract ▼ AI-Helper

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 lidar-derived 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.

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Robust Interpolation of DEMs From Lidar-Derived Elevation Data

Abstract ▼ AI-Helper

참고문헌 (55)

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