Hybrid Overlap Filter for LiDAR Point Clouds Using Free Software

Abstract

Despite the large amounts of resources destined to developing filtering algorithms of LiDAR point clouds in order to obtain a Digital Terrain Model (DTM), the task remains a challenge. As a society advancing towards the democratization of information and collaborative processes, the researchers should not only focus on improving the efficacy of filters, but should also consider the users’ needs with a view toward improving the usability and accessibility of the filters in order to develop tools that will provide solutions to the challenges facing this field of study. In this work, we describe the Hybrid Overlap Filter (HyOF), a new filtering algorithm implemented in the free R software environment. The flow diagram of HyOF differs in the following ways from that of other filters developed to date: (1) the algorithm is formed by a combination of sequentially operating functions (i.e., the output of the first function provides the input of the second), which are capable of functioning independently and thus enabling integration of these functions with other filtering algorithms; (2) the variable penetrability is defined and used, along with slope and elevation, to identify ground points; (3) prior to selection of the seed points, the original point cloud is processed with the aim of removing points corresponding to buildings; and (4) a new method based on a moving window, with longitudinal overlap between windows and transverse overlap between passes, is used to select the seed points. Our hybrid filtering method is tested using 15 reference samples acquired by the International Society of Photogrammetry and Remote Sensing (ISPRS) and is evaluated in comparison with 33 existing filtering algorithms. The results show that our hybrid filtering method produces an average total error of 3.34% and an average Kappa coefficient of 92.62%. The proposed algorithm is one of the most accurate filters that has been tested with the ISPRS reference samples.

Comparison of Filters for Archaeology-Specific Ground Extraction from Airborne LiDAR Point Clouds

“…These and numerous other existing filters are based on algorithms that can be divided into five categories [26,[45][46][47]:…”

“…Morphological filtering algorithms are based on the concept of mathematical morphology. They retain terrain details by comparing the elevation of neighboring points [46,47]. A point is classified as ground if its height does not exceed the height of the eroded surface.…”

“…The new points are inserted into the triangulation. The process is repeated on the thus densified network until (i) there are no more points in a triangle, (ii) a certain density of ground points is reached, or (iii) when all acceptable points are closer to the surface than a threshold value [26,46,47].…”

“…Hybrid filters combine algorithms to merge the strengths of different approaches [46,47]. The filters used in this article and their specific implementation in the software are described in more detail in Appendix A.…”

“…The segmentation-based algorithms have advantages mostly in urban areas where flat segments are prevailing [46]. Bujan recommends that morphological filtering algorithms are best suited for terrains with small objects, progressive densification for urban areas and terrains with diverse objects, while surface-and segmentation-based algorithms work best in forests and on steep slopes [47]. To this, we may add that one of the key features of segmentation-based algorithms is that the outliers (low and high) theoretically have a limited impact on the result.…”