An Analysis of Ground-Point Classifiers for Terrestrial LiDAR
LiDAR ground-point classification (GPC) is a preprocessing technique required for many applications of LiDAR data. Previous literature has compared the performance of existing GPC techniques on airborne LiDAR (ALS) data, however, their performance when applied to terrestrial LiDAR (TLS) data has not yet been addressed. This research tested the classification accuracy of five openly-available GPC algorithms on seven TLS datasets. Classification performance was analyzed using the Kappa Index of Agreement (KIA) and rasterized spatial distribution of classification accuracy datasets generated through comparisons to manually classified reference datasets. The results identified that, for certain classifiers, classification accuracy decreased for the classification of low vegetation, variably sloped terrain, low outlier points, and OT points without any ground points beneath. Additionally, the results show that while no single algorithm is suitable for use on all datasets containing varying terrain characteristics and OT object types, in general, a mathematical morphology/slope-based method outperformed other methods.