Multidirectional Shift Rasterization (MDSR) Algorithm for Effective Identification of Ground in Dense Point Clouds

Abstract

With the ever-increasing popularity of unmanned aerial vehicles and other platforms providing dense point clouds, filters for the identification of ground points in such dense clouds are needed. Many filters have been proposed and are widely used, usually based on the determination of an original surface approximation and subsequent identification of points within a predefined distance from such surface. We presented a new filter, the multidirectional shift rasterization (MDSR) algorithm, which is based on a different principle, i.e., on the identification of just the lowest points in individual grid cells, shifting the grid along both the planar axis and subsequent tilting of the entire grid. The principle was presented in detail and both visually and numerically compared with other commonly used ground filters (PMF, SMRF, CSF, and ATIN) on three sites with different ruggedness and vegetation density. Visually, the MDSR filter showed the smoothest and thinnest ground profiles, with the ATIN the only filter comparably performing. The same was confirmed when comparing the ground filtered by other filters with the MDSR-based surface. The goodness of fit with the original cloud is demonstrated by the root mean square deviations (RMSDs) of the points from the original cloud found below the MDSR-generated surface (ranging, depending on the site, between 0.6 and 2.5 cm). In conclusion, this paper introduced a newly developed MDSR filter that outstandingly performed at all sites, identifying the ground points with great accuracy while filtering out the maximum of vegetation and above-ground points and outperforming the aforementioned widely used filters. The filter dilutes the cloud somewhat; in such dense point clouds, however, this can be perceived as a benefit rather than as a disadvantage.

Development of a river DTM generation algorithm based on SfM point clouds using vegetation and morphological filters

“…Moreover, CSF showed superior vegetation removal capability compared to ATIN, which contradicts the findings of [22,29] who did not undergo the optimization process of CSF parameters. These results highlight the importance of selecting optimal parameters for accurate ground extraction.…”

“…It also faces difficulties in capturing accurate DTMs in areas with dense vegetation [18]. Methods for generating DTMs from point clouds can be broadly classified into morphological filtering methods [21][22][23][24][25][26][27][28][29], vegetation index-based methods [30][31][32][33][34], composite methods [35][36][37][38], and machine learning-based methods [39][40][41] [35] effectively removed vegetation in steep and rugged terrains using morphological filters such as Progressive Morphological Filter (PMF), Simple Morphological Filter (SMRF), Cloth Simulation Filter (CSF), Adaptive Triangular Irregular Network (ATIN), and CAractérisation de NUages de POints (CANUPO). [42] achieved vegetation removal using CSF and ATIN.…”

Complex Methodology for Spatial Documentation of Geomorphological Changes and Geohazards in the Alpine Environment

“…A significant disadvantage of these data is the rarely updated sparse point cloud. From data obtained by UAS photogrammetry, TLS, and ALS methods, the RMSE of height differences in the entire compared area was determined to be 20 mm with a systematic shift of 6 mm [50]. When comparing point clouds from UAS photogrammetry and ALS, the average systematic shift had a value of 1 mm, with an RMSE value of 46 mm.…”

Detecting Historical Terrain Anomalies With UAV-LiDAR Data Using Spline-Approximation and Support Vector Machines

“…the terrain anomalies of interest in our study area. The final classification threshold is then set to 0.1 m similar to comparable study sites with steep/sloping terrain in other related studies [71], [73].…”