Parametric investigation of the performance of lidar filters using different surface contexts

Lidar technology has provided an accurate and efficient way to obtain digital elevation models. While digital terrain models (DTMS) are essential products for three-dimensional spatial applications, extraction of ground points from a mixture of ground and non-ground points is not straightforward, and interactive classification of massive point data sets is prohibitive. To automate the filtering process, many algorithms have been proposed and demonstrated to produce satisfactory results when applied with suitably tuned parameters. For obtaining quality products using lidar filters, however, not only to figure out their optimal performance, but also to analyze the cause and effect relationships between filtering steps and their effects under variable conditions is important. Hence, this study examined the performance of three popular surface models for lidar data filtering: morphological operations, triangulation, and linear prediction. For the test, consistent setting of parameters was applied across considerably different landscape datasets. The strengths and weaknesses of the test filters were investigated by comparing the metrics of omission and commission errors and volumetric distortions, and by observing resulting DTMs and relevant surface profiles.