Occlusion-based Methodology for the Classification of Lidar Data

Lidar systems have been widely adopted for the acquisition of dense and accurate topographic data over extended areas. The level of detail and the quality of the collected point cloud motivated the research community to investigate the possibility of automatic object extraction from such data. Prior knowledge of the terrain surface will improve the performance of object detection and extraction procedures. In this paper, a new strategy for automatic terrain extraction from lidar data is presented. The proposed strategy is based on the fact that sudden elevation changes, which usually correspond to non-ground objects, will cause relief displacements in perspective views. The introduced relief displacements will occlude neighboring ground points. To start the process, we generate a digital surface model (DSM) from the irregular lidar points using an interpolation procedure. The presence of sudden-elevation changes and the resulting occlusions can be discerned by sequentially checking the off-nadir angles to the lines of sight connecting the DSM cells and a pre-defined set of synthesized projection centers. Detected occlusions are then used to identify the occluding points, which are hypothesized to be non-ground points. Surface roughness and discontinuities together with inherent noise in the point cloud null lead to some false hypotheses. Therefore, we use a statistical filter to remove these false hypotheses. The performance of the algorithm has been evaluated and verified using both simulated and real lidar datasets with varying levels of complexity.