Abstract
Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third step is our main contribution. 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. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. 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. 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.
Highlights
Airborne LiDAR (Light Detection and Ranging), termed Airborne Laser Scanning (ALS), is a widely employed technology to capture the 3D geometry of the Earth ground surface and the Remote Sens. 2014, 6 objects on it [1]
We propose a segmentation-based filtering (SBF) approach, which combines a point cloud segmentation method and the classic
Results of Sample 11, Sample 24, Sample 51, Sample 53, Sample 71 and Sample 12 are displayed to reflect the details of filtering, as shown in Figures 7–12 respectively
Summary
Airborne LiDAR (Light Detection and Ranging), termed Airborne Laser Scanning (ALS), is a widely employed technology to capture the 3D geometry of the Earth ground surface and the Remote Sens. 2014, 6 objects on it [1]. In most ALS applications, filtering is a necessary step to determine which LiDAR returns are from the ground surface and which are from the off-terrain objects [16]. An experimental comparison of the performance of eight filter algorithms was presented by Sithole and Vosselman [17]. They concluded that, surface-based filters often yield better results concerning the filter strategy, because they use more context than other filter strategies. Among surface-based filtering methods, progressive TIN (triangular irregular network) densification (PTD) is widely employed in both the scientific community and engineering applications, because it has been integrated into the commercial software TerraSolid. It often fails to detect the terrain points on break lines and step edges, and mistakes the lower parts of objects as ground ones, as shown in Figure 1b,d respectively
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