Abstract
Airborne light detection and ranging (LiDAR) datasets with a large volume pose a great challenge to the traditional interpolation methods for the production of digital terrain models (DTMs). Thus, a fast, global interpolation method based on thin plate spline (TPS) is proposed in this paper. In the methodology, a weighted version of finite difference TPS is first developed to deal with the problem of missing data in the grid-based surface construction. Then, the interpolation matrix of the weighted TPS is deduced and found to be largely sparse. Furthermore, the values and positions of each nonzero element in the matrix are analytically determined. Finally, to make full use of the sparseness of the interpolation matrix, the linear system is solved with an iterative manner. These make the new method not only fast, but also require less random-access memory. Tests on six simulated datasets indicate that compared to recently developed discrete cosine transformation (DCT)-based TPS, the proposed method has a higher speed and accuracy, lower memory requirement, and less sensitivity to the smoothing parameter. Real-world examples on 10 public and 1 private dataset demonstrate that compared to the DCT-based TPS and the locally weighted interpolation methods, such as linear, natural neighbor (NN), inverse distance weighting (IDW), and ordinary kriging (OK), the proposed method produces visually good surfaces, which overcome the problems of peak-cutting, coarseness, and discontinuity of the aforementioned interpolators. More importantly, the proposed method has a similar performance to the simple interpolation methods (e.g., IDW and NN) with respect to computing time and memory cost, and significantly outperforms OK. Overall, the proposed method with low memory requirement and computing cost offers great potential for the derivation of DTMs from large-scale LiDAR datasets.
Highlights
There is little doubt that the acquisition and availability of airborne light detection and ranging (LiDAR) datasets have revolutionized the construction of high-quality digital terrain models (DTMs) [1,2,3], which play an important role in supporting wide range of applications, including engineering projects, geomorphology, hydrology management, and disaster assessment and planning [4]
We propose a fast, global interpolation method based on finite difference thin plate spline (TPS) for DTM derivation from large LiDAR datasets
To efficiently generate high-quality DTMs from large LiDAR-derived datasets, we propose a fast, global interpolation method in this paper
Summary
There is little doubt that the acquisition and availability of airborne light detection and ranging (LiDAR) datasets have revolutionized the construction of high-quality digital terrain models (DTMs) [1,2,3], which play an important role in supporting wide range of applications, including engineering projects, geomorphology, hydrology management, and disaster assessment and planning [4]. One promising feature of LiDAR point clouds is the high-density with a large amount of data points. This imposes a great challenge on most traditional interpolation methods for the construction of high-resolution and high-quality DTMs [5]. A variety of interpolation methods have been adopted in geosciences for LiDAR-derived DTM generation, such as inverse distance weighting (IDW), triangulated irregular network (TIN). Doucette and Beard [10] compared the performance of kriging, TPS, IDW, and surface trend analysis for filling DTM gaps, and found that TPS is more favorable than the others. Desmet [11] showed that the best interpolation method for DTM construction in the selected study area is TPS. In the context of LiDAR point cloud filtering, TPS was often employed to generate reference ground surfaces [13,14,15,16,17,18]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
