Abstract
Separating point clouds into ground and non-ground points is a preliminary and essential step in various applications of airborne light detection and ranging (LiDAR) data, and many filtering algorithms have been proposed to automatically filter ground points. Among them, the progressive triangulated irregular network (TIN) densification filtering (PTDF) algorithm is widely employed due to its robustness and effectiveness. However, the performance of this algorithm usually depends on the detailed initial terrain and the cautious tuning of parameters to cope with various terrains. Consequently, many approaches have been proposed to provide as much detailed initial terrain as possible. However, most of them require many user-defined parameters. Moreover, these parameters are difficult to determine for users. Recently, the cloth simulation filtering (CSF) algorithm has gradually drawn attention because its parameters are few and easy-to-set. CSF can obtain a fine initial terrain, which simultaneously provides a good foundation for parameter threshold estimation of progressive TIN densification (PTD). However, it easily causes misclassification when further refining the initial terrain. To achieve the complementary advantages of CSF and PTDF, a novel filtering algorithm that combines cloth simulation (CS) and PTD is proposed in this study. In the proposed algorithm, a high-quality initial provisional digital terrain model (DTM) is obtained by CS, and the parameter thresholds of PTD are estimated from the initial provisional DTM based on statistical analysis theory. Finally, PTD with adaptive parameter thresholds is used to refine the initial provisional DTM. These contributions of the implementation details achieve accuracy enhancement and resilience to parameter tuning. The experimental results indicate that the proposed algorithm improves performance over their direct predecessors. Furthermore, compared with the publicized improved PTDF algorithms, our algorithm is not only superior in accuracy but also practicality. The fact that the proposed algorithm is of high accuracy and easy-to-use is desirable for users.
Highlights
Among the surface-based methods, progressive triangulated irregular network (TIN) densification filtering (PTDF) is one of the typical methods that construct the initial TIN-based digital terrain model (DTM) from ground seed points, which are the lowest points in each grid cell of an entire region dataset
To make use of the advantages of PTDF and cloth simulation filtering (CSF), this study proposes a novel algorithm to differentiate between ground and non-ground points from airborne light detection and ranging (LiDAR) point clouds in a high-precision and easy-to-use manner by combining cloth simulation (CS) and progressive TIN densification (PTD)
We present a high accuracy and easy-to-use filtering algorithm
Summary
Airborne light detection and ranging (LiDAR) technology, which is an efficient and rapid remote sensing technology for collecting three-dimensional (3D) point clouds over a large area, has been widely employed in various fields, such as digital terrain model (DTM) generation [1,2,3,4,5], forest ecosystem investigation [6,7,8,9,10,11,12], and 3D building modeling [13,14,15,16]. An experimental comparison of eight filtering algorithms was performed by Sithole and Vosselman [2] They concluded that the surface-based methods generally performed better than other filtering methods because they used more context information. The basic principle of surface-based methods is to gradually approximate the bare earth using a parametric surface, such as triangulated irregular network (TIN) model, weighted linear least-squares interpolation model, active shape model, thin plate spline (TPS), and cloth simulation model. For this type of method, the ground seed points are obtained and densified iteratively to create a provisional DTM that gradually refines the ground surface based on certain criteria (e.g., elevation). It has been integrated into multiple software packages, such as Terrasolid, Lastools, and ALDPAT [37,45,46]
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