In this paper, a graph-based technique originally intended for image processing has been tailored for the segmentation of airborne LiDAR points, that are irregularly distributed. Every LiDAR point is labeled as a node and interconnected as a graph extended to its neighborhood and defined in a 4D feature space (x, y, z, and the reflection intensity). The interconnections between pairs of neighboring nodes are weighted based on the distance in the feature space. The segmentation consists in an iterative process of classification of nodes into homogeneous groups based on their similarity. This approach is intended to be part of a complete system for classification of structures from LiDAR point clouds in applications needing fast response times. In this sense, a study of the performance/accuracy trade-off has been performed, extracting some conclusions about the benefits of the proposed solution. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Keywords: Airborne LiDAR, Point-cloud segmentation, Graph processing