3D GEOLOGICAL OUTCROP CHARACTERIZATION: AUTOMATIC DETECTION OF 3D PLANES (AZIMUTH AND DIP) USING LiDAR POINT CLOUDS

Abstract

ABSTRACT:Terrestrial laser scanning constitutes a powerful method in spatial information data acquisition and allows for geological outcrops to be captured with high resolution and accuracy. A crucial aspect for numerous geologic applications is the extraction of rock surface orientations from the data. This paper focuses on the detection of planes in rock surface data by applying a segmentation algorithm directly to a 3D point cloud. Its performance is assessed considering (1) reduced spatial resolution of data and (2) smoothing in the course of data pre-processing. The methodology is tested on simulations of progressively reduced spatial resolution defined by varying point cloud density. Smoothing of the point cloud data is implemented by modifying the neighborhood criteria during normals estimation. The considerable alteration of resulting planes emphasizes the influence of smoothing on the plane detection prior to the actual segmentation. Therefore, the parameter needs to be set in accordance with individual purposes and respective scales of studies. Furthermore, it is concluded that the quality of segmentation results does not decline even when the data volume is significantly reduced down to 10%. The azimuth and dip values of individual segments are determined for planes fit to the points belonging to one segment. Based on these results, azimuth and dip as well as strike character of the surface planes in the outcrop are assessed. Thereby, this paper contributes to a fully automatic and straightforward workflow for a comprehensive geometric description of outcrops in 3D.

Assessment of Accuracy in the Identification of Rock Formations from Aerial and Terrestrial Laser-Scanning Data

“…But it is still unlikely to solve the issue of overhangs, which are more conveniently scanned with other methods (such as terrestrial station, remote-piloted aircraft systems). This method for processing supplementary data would be possible even under difficult situations as shown in [12]. One possible error in testing and modeling the Bílá skála object is the varying point cloud density.…”

“…One possible error in testing and modeling the Bílá skála object is the varying point cloud density. In the next stage of the research, this could be reworked into a better model, but the cost would be increased calculation time [12].…”

“…Altitude, slope, slope orientation, and relief energy were selected as the main search parameters in the DTM [11]. Automatic facet detection on rocky surfaces was investigated in [12]. During identification, the varying density of the point cloud and its influence on the success of classification was tested.…”

An MFF-SLIC Hybrid Superpixel Segmentation Method with Multi-Source RS Data for Rock Surface Extraction

“…The greater the value of m, the more important spatial proximity and the more compact the cluster. Experience indicates that the optimal range of values for m is [1,20], which can get a good tradeoff between color similarity and spatial proximity [38].…”

“…At present, there is only so much research on point cloud or image segmentation, which would be valuable for the segmentation of multi-source RS data integration. The point cloud segmentation methods include edge-based segmentation [10][11][12], model fitting-based segmentation [13][14][15], region-based segmentation [16][17][18][19][20] and feature clustering-based segmentation [21,22]. For the point cloud, the only spatial information is insufficient for segmentation.…”