Point cloud filtering on UAV based point cloud
Citation
Zeybek, M., & Şanlıoğlu, İ. (2019). Point cloud filtering on UAV based point cloud. Measurement, 133, 99-111.Abstract
Nowadays, Unmanned Aerial Vehicles (UAVs) have been attracted wide attentions such as a new measurement equipment and mapping, which are capable of the high-resolution point cloud data collection.
In addition, a massive point cloud data has brought about the data filtering and irregular data organization for the generation of digital terrain models. Filtering of point clouds contains vegetations and artificial objects play a crucial role for bare earth terrain modelling. Topographical maps rely on the data
structures which are built on bare ground terrain points. The bare earth surface extraction is not the only
crucial to the topographical maps but also decision-making processes such as natural hazards management, deformation analysis and interpretation.
In order to filter a UAV-based 3D raw point cloud data, in this paper, filtering performance of four different algorithms using open source and commercial software’s have been investigated, (1) curvature
based (Multiscale Curvature Classification-MCC), (2) surface-based filtering (FUSION), (3) progressive
TIN based (LasTool-LasGround module-commercial) and (4) physical simulation processing (Cloth
Simulation Filtering-CSF). The applied filtering results were validated with the reference data set classified by operator. Although different filtering methodologies implemented on point clouds, these methods
demonstrated similar results to extract ground on distinctive terrain feature such as dense vegetated, flat
surface, rough and complex landscapes. The filtering algorithms’ results revealed that UAV-generated
data suitable for extraction of bare earth surface feature on the different type of a terrain. Accuracy of
the filtered point cloud reached the 93% true classification on flat surfaces from CSF filtering method