Graph-based approach for airborne light detection and ranging segmentation

TLDR

A graph‑based segmentation technique has been tailored to segment irregularly distributed airborne LiDAR points. The method is intended to serve as part of a fast‑response system for classifying structures from LiDAR point clouds and proposes an interlaced graph approach to enhance reliability. Each LiDAR point is treated as a node in a 4‑D feature graph (x, y, z, intensity), with edges weighted by feature distance, and nodes are iteratively classified into homogeneous groups; an interlaced variant further improves segmentation. Performance‑accuracy trade‑off analysis shows that the proposed graph‑based segmentation offers tangible benefits.

Abstract

A graph-based segmentation technique has been tailored to segment airborne LiDAR points which, unlike images, are irregularly distributed. In our method, every LiDAR point is labeled as a node and interconnected as a graph extended to its neighborhood, defined in a 4-D feature space: the spatial coordinates (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 of 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 the 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. In addition, an interlaced graph-based approach is proposed to increase the reliability in general purpose segmentations.