Rock Surface Classification in a Mine Drift Using Multiscale Geometric Features

Scale-dependent statistical depictions of surface morphology offer the potential to parameterize complex geometrical scaling relationships with greater detail than traditional fractal measures. Using multiscale operators, it is possible to identify points belonging to rough discontinuous surfaces in noisy point clouds solely on the basis of their local geometry. Many strategies for point cloud feature classification have been developed since the proliferation of laser scanning systems. Most of the techniques which are applicable to natural scenes employ external data sources such as hyperspectral imagery, return pulse intensity, and waveform data. In this letter, multiscale geometric parameters are used to identify individual point observations corresponding to rock surfaces in point clouds acquired by terrestrial laser scanning in scenes with man-made clutter and scanning artifacts. Three multiscale operators, namely, the approximate shape and density of a defined neighborhood and the distance of its mean point from its geometric center, are fused into a single feature vector. The procedure is demonstrated using real point cloud data acquired in a mine drift, with the goal of identifying points belonging to the rock face obscured by an overlying wire support mesh. Using the extra-trees classifier, extraneous returns caused by the mesh were excluded from the point cloud with a 97% success rate, while 87% of the desired surface points were retained.