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Proceedings Paper

Feature extraction from 3D lidar point clouds using image processing methods
Author(s): Ling Zhu; Ashton Shortridge; David Lusch; Ruoming Shi
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Paper Abstract

Airborne LiDAR data have become cost-effective to produce at local and regional scales across the United States and internationally. These data are typically collected and processed into surface data products by contractors for state and local communities. Current algorithms for advanced processing of LiDAR point cloud data are normally implemented in specialized, expensive software that is not available for many users, and these users are therefore unable to experiment with the LiDAR point cloud data directly for extracting desired feature classes. The objective of this research is to identify and assess automated, readily implementable GIS procedures to extract features like buildings, vegetated areas, parking lots and roads from LiDAR data using standard image processing tools, as such tools are relatively mature with many effective classification methods. The final procedure adopted employs four distinct stages. First, interpolation is used to transfer the 3D points to a high-resolution raster. Raster grids of both height and intensity are generated. Second, multiple raster maps - a normalized surface model (nDSM), difference of returns, slope, and the LiDAR intensity map - are conflated to generate a multi-channel image. Third, a feature space of this image is created. Finally, supervised classification on the feature space is implemented. The approach is demonstrated in both a conceptual model and on a complex real-world case study, and its strengths and limitations are addressed.

Paper Details

Date Published: 13 September 2011
PDF: 11 pages
Proc. SPIE 8159, Lidar Remote Sensing for Environmental Monitoring XII, 81590P (13 September 2011); doi: 10.1117/12.893856
Show Author Affiliations
Ling Zhu, Beijing Univ. of Civil Engineering and Architecture (China)
Michigan State Univ. (United States)
Ashton Shortridge, Michigan State Univ. (United States)
David Lusch, Michigan State Univ. (United States)
Ruoming Shi, Beijing Univ. of Civil Engineering and Architecture (China)

Published in SPIE Proceedings Vol. 8159:
Lidar Remote Sensing for Environmental Monitoring XII
Upendra N. Singh, Editor(s)

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