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.
David L. Vilariño, Jorge Martínez, Francisco F. Rivera, José C. Cabaleiro, and Tomás F. Pena, "Graph-based segmentation of airborne lidar point clouds," Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040I (Presented at SPIE Remote Sensing: September 27, 2016; Published: 18 October 2016); https://doi.org/10.1117/12.2242001.
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