Abstract
The progressive TIN (triangular irregular network) densification (PTD) filter algorithm is widely used for filtering point clouds. In the PTD algorithm, the iterative densification parameters become smaller over the entire process of filtering. This leads to the performance—especially the type I errors of the PTD algorithm—being poor for point clouds with high density and standard variance. Hence, an improved PTD filtering algorithm for point clouds with high density and variance is proposed in this paper. This improved PTD method divides the iterative densification process into two stages. In the first stage, the iterative densification process of the PTD algorithm is used, and the two densification parameters become smaller. When the density of points belonging to the TIN is higher than a certain value (in this paper, we define this density as the standard variance intervention density), the iterative densification process moves into the second stage. In the second stage, a new iterative densification strategy based on multi-scales is proposed, and the angle threshold becomes larger. The experimental results show that the improved PTD algorithm can effectively reduce the type I errors and total errors of the DIM point clouds by 7.53% and 4.09%, respectively, compared with the PTD algorithm. Although the type II errors increase slightly in our improved method, the wrongly added objective points have little effect on the accuracy of the generated DSM. In short, our improved PTD method perfects the classical PTD method and offers a better solution for filtering point clouds with high density and standard variance.
Highlights
Point clouds (including light detection and ranging (LiDAR) point clouds and dense image matching (DIM) point clouds have been widely used in various fields, such as land cover classification [1], canopy detection and vegetation analysis [2,3], the reconstruction of digital terrain models (DTM) [4], etc
According to the commercial software TerraSolid, five key parameters should be determined in the PTD algorithm: max building size, which determines the size of grid cell; terrain angle, which decides whether adopts the mirror technology; iteration angle, which is the maximum angle between the TIN facet and a line that links an unclassified point to the closest vertex of the facet; iteration distance, which is the maximum distance from an unclassified point to the corresponding TIN facet; and Edge length, which represents the minimum threshold for the maximum edge length of TIN facet
Our proposed method focuses on the impact of the density and standard variance of point clouds on the performance of the PTD algorithm
Summary
Point clouds (including light detection and ranging (LiDAR) point clouds and dense image matching (DIM) point clouds have been widely used in various fields, such as land cover classification [1], canopy detection and vegetation analysis [2,3], the reconstruction of digital terrain models (DTM) [4], etc. To remove the ground points from point clouds with high density completely, researchers [1,32,33,34] rarefied the point clouds with high density and standard variance at first; used the PTD algorithm to obtain the ground points and construct the digital terrain model (DTM); and, calculated the distance from an unclassified point to the DTM If this distance is lower than a given threshold, this point is regarded as a ground point; otherwise, it is a non-ground point. This paper analyzes how the density and standard variance of point clouds impact the filtering performance of the PTD method.
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