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Proceedings Paper

Graph-based segmentation of airborne lidar point clouds
Author(s): David L. Vilariño; Jorge Martínez; Francisco F. Rivera; José C. Cabaleiro; Tomás F. Pena
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Paper Abstract

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.

Paper Details

Date Published: 18 October 2016
PDF: 8 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100040I (18 October 2016); doi: 10.1117/12.2242001
Show Author Affiliations
David L. Vilariño, Univ. de Santiago de Compostela (Spain)
Jorge Martínez, Univ. de Santiago de Compostela (Spain)
Francisco F. Rivera, Univ. de Santiago de Compostela (Spain)
José C. Cabaleiro, Univ. de Santiago de Compostela (Spain)
Tomás F. Pena, Univ. de Santiago de Compostela (Spain)

Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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