A Thin Plate Spline-Based Feature-Preserving Method for Reducing Elevation Points Derived from LiDAR

Remote Sensing

Chuanfa Chen⁴  Yanyan Li²  Changqing Yan³  Honglei Dai¹  Guolin Liu¹  Nicolas Baghdadi⁵ 

[1] Shandong Provincial Key Laboratory of Geomatics and Digital Technology, Shandong University of Science and Technology, Qingdao 266590, China; E-Mails:;Shool of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China; E-Mail:;Department of Information Engineering, Shandong University of Science and Technology, Tai’an 271019, China; E-Mail:;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 266590, China;id="af1-remotesensing-07-11344">1State 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 266590, Chi

关键词: data reduction;    thin plate spline;    interpolation;    DEM;    LiDAR;    terrain feature;   

DOI  :  10.3390/rs70911344

来源: mdpi

【 摘 要 】

Light detection and ranging (LiDAR) technique is currently one of the most important tools for collecting elevation points with a high density in the context of digital elevation model (DEM) construction. However, the high density data always leads to serious time and memory consumption problems in data processing. In this paper, we have developed a thin plate spline (TPS)-based feature-preserving (TPS-F) method for LiDAR-derived ground data reduction by selecting a certain amount of significant terrain points and by extracting geomorphological features from the raw dataset to maintain the accuracy of constructed DEMs as high as possible, while maximally keeping terrain features. We employed four study sites with different topographies (i.e., flat, undulating, hilly and mountainous terrains) to analyze the performance of TPS-F for LiDAR data reduction in the context of DEM construction. These results were compared with those of the TPS-based algorithm without features (TPS-W) and two classical data selection methods including maximum z-tolerance (Max-Z) and the random method. Results show that irrespective of terrain characteristic, the two versions of TPS-based approaches (i.e., TPS-F and TPS-W) are always more accurate than the classical methods in terms of error range and root means square error. Moreover, in terms of streamline matching rate (SMR), TPS-F has a better ability of preserving geomorphological features, especially for the mountainous terrain. For example, the average SMR of TPS-F is 89.2% in the mountainous area, while those of TPS-W, max-Z and the random method are 56.6%, 34.7% and 35.3%, respectively.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.