![]() In this way, the triangulation was progressively densified until all points were classified as ground or object. Before continuing with the next iteration, all ground points were added to the TIN. If a point was found with offsets below the threshold values, it was classified as a ground point and the algorithm proceeds with the next triangle. The parameters were the distance to the TIN facets and the angles to the nodes. ![]() For each triangle, one additional ground point was determined by investigating the parameters of the unclassified points in each triangle with the reference surface. Axelsson first divided the whole point dataset into tiles, and then selected the lowest points in each block as the initial ground points, and finally a TIN of the identified ground points was constructed as the reference surface. Depending on the means of creating the surface, surface-based filtering methods can be further divided into three subcategories: morphology-based filters, iterative-interpolation-based filters, and progressive-densification-based filters. Thus, the core step of this kind of methods is to create a surface approximating the bare earth. In surface-based approaches, the basic idea is to create a parametric surface with a corresponding buffer zone above it, the surface locates the buffer zone, and as before the buffer zone defines a region in 3D space where ground points are expected to reside. However, the main difference between our method and the classic PDT method is that a segment instead of a single point is the basic processing unit. At last, segments are classified either as ground or object segments based on the principles of the classic PDT method (see Section 3.1). In the second step, an analysis of multiple echoes is performed to remove the vegetation measurements (see Section 3.3). The first step of our filtering approach is point cloud segmentation for the point cloud (see Section 3.2). In this article, we propose a segmentation-based filtering (SBF) approach (see Section 3), which combines a point cloud segmentation method and the classic PTD’s framework. However, Hutton and Brazier emphasized that errors in filtering and interpolation will affect subsequent use – for example using terrain in a hydraulic model, or vegetation height estimation in biomass calculation. To conclude, although many methods have been developed to tackle the filtering problem, it has not been fully solved so far. ![]() As a result, the SBF approach is able to reduce omission errors and total errors by 18.26% and 11.47% respectively, which would significantly decrease the cost of manual operation required in post-processing. Experimental results suggest that, compared with the PTD method, the SBF approach is capable of preserving discontinuities of landscapes and removing the lower parts of large objects attached on the ground surface. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. Particularly, the iterative judgment is based on the framework of the classic progressive TIN (triangular irregular network) densification (PTD) method, but with basic processing unit being a segment rather than a single point. Moreover, the third step is our main contribution. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. A segmentation-based filtering (SBF) method is proposed herein. Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds.
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