Filtering Airborne LIDAR Data Using Simple Triangulation Irregular Network

In recent years, airborne and terrestrial laser scanning systems have become increasingly popular for obtaining geospatial information. High-quality 3D point clouds are used for a wide range of applications, with digital terrain models (DTMs) representing one of these products. Light Detection and Ranging (LiDAR) data is widely used in producing digital elevation models (DEM), which are fundamental elements of planning applications. Removing non-ground objects from LiDAR point clouds is the main problem of the DTM production workflow. Triangulated irregular network (TIN) densification is a classical technique among LiDAR filtering algorithms. In this study, a simple filtering algorithm entitled simple TIN densification (sTIN) is proposed, which is derived from classic TIN densification. The performance of sTIN is tested with three filters, namely, the adaptive triangulated irregular network, the simple morphological filter and the improved progressive TIN densification. The proposed algorithm has an average type I error rate of 5.6%, an average type II error rate of 10.42% and an average total error rate of 8.2%. In addition, the strengths and weaknesses of the algorithms are examined with regards to the root-mean-square error (RMSE) values and visual analyses of DTMs.