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Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors

Liu, Yuee, Li, Zhengrong, Hayward, Ross F., Walker, Rodney A., & Jin, Hang (2009) Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors. In Proceedings of the Digital Image Computing : Techniques and Applications Conference (DICTA 2009), IEEE Computer Society, Melbourne, Victoria. (In Press)

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Abstract

Light Detection and Ranging (LIDAR) has great potential to assist vegetation management in power line corridors by providing more accurate geometric information of the power line assets and vegetation along the corridors. However, the development of algorithms for the automatic processing of LIDAR point cloud data, in particular for feature extraction and classification of raw point cloud data, is in still in its infancy. In this paper, we take advantage of LIDAR intensity and try to classify ground and non-ground points by statistically analyzing the skewness and kurtosis of the intensity data. Moreover, the Hough transform is employed to detected power lines from the filtered object points. The experimental results show the effectiveness of our methods and indicate that better results were obtained by using LIDAR intensity data than elevation data.

Impact and interest:

5 citations in Scopus
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0 citations in Web of Science®

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Full-text downloads:

226 since deposited on 07 Dec 2009
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ID Code: 29123
Item Type: Conference Paper
Keywords: LiDAR, Point cloud processing, Statistical analysis, Hough transform, power line detection
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2009 Please consult the authors.
Deposited On: 07 Dec 2009 14:00
Last Modified: 01 Mar 2012 09:39

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