Semi-automatic extraction of rock discontinuities from point clouds using the ISODATA clustering algorithm and deviation from mean elevation

Abstract

This paper presents a methodology for semi-automatic extraction of rock discontinuities from point clouds on the resulting 3D digital surface models of rock masses acquired by terrestrial laser scanning or photogrammetry. First, point clouds need to be pre-processed to generate a digital elevation model from a 3D digital surface model of a rock mass. Second, co-directional features of rock discontinuities are identified by classifying the standard deviation of the elevation, slope aspect and slope gradient of raster grids in a digital elevation model using the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA). Third, planar features of rock discontinuities are inspected by judging whether the value range of the deviation from the mean elevation (DEV) in the classified subset of raster grids is between + 1 and − 1 using the Topographic Position Index of terrain analysis. Fourth, point clouds data corresponding to the target surfaces of rock discontinuities are acquired by data post-processing using the ArcToolbox of ArcGIS. Finally, geological analysis is executed by calculating the geometric features of the extracted point clouds. A case study of a rock slope along a highway is presented using the proposed method, the results of which demonstrate that the proposed method is so rapid and accurate that it could greatly reduce the amount of manual labour, improve the efficiency of surveying, and utilize the powerful terrain analysis and visualization capabilities of ArcGIS to avoid the computational challenges associated with directly analysing point cloud data.