LiDAR data are available in a variety of publicly-accessible forums, providing high-resolution, accurate 3- dimensional information about objects at the Earth's surface. Automatic extraction of information from LiDAR point clouds, however, remains a challenging problem. The focus of this research is to develop methods for point cloud classification and object detection which can be customized for specific applications. The methods presented rely on analysis of statistics of local neighborhoods of LiDAR points. A multi-dimensional vector composed of these statistics can be classified using traditional data classification routines. Local neighborhood statistics are defined, and examples are given of the methods for specific applications such as building extraction and vegetation classification. Results indicate the feasibility of the local neighborhood statistics approach and provide a framework for the design of customized classification or object detection routines for LiDAR point clouds.

Date Published: 20 May 2013
PDF: 10 pages
Proc. SPIE 8731, Laser Radar Technology and Applications XVIII, 873103 (20 May 2013); doi: 10.1117/12.2015709

Published in SPIE Proceedings Vol. 8731:
Laser Radar Technology and Applications XVIII
Monte D. Turner; Gary W. Kamerman, Editor(s)