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Proceedings Paper

Aerial lidar data classification using expectation-maximization
Author(s): Suresh K. Lodha; Darren M. Fitzpatrick; David P. Helmbold
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Paper Abstract

We use the Expectation-Maximization (EM) algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged). We apply and test our results using ten regions taken from lidar data collected over an area of approximately eight square miles, obtaining higher than 94% accuracy. We also apply our classifier to our entire dataset, and present visual classification results both with and without uncertainty. We use several approaches to evaluate the parameter and model choices possible when applying EM to our data. We observe that our classification results are stable and robust over the various subregions of our data which we tested. We also compare our results here with previous classification efforts using this data.

Paper Details

Date Published: 29 January 2007
PDF: 11 pages
Proc. SPIE 6499, Vision Geometry XV, 64990L (29 January 2007); doi: 10.1117/12.714713
Show Author Affiliations
Suresh K. Lodha, Univ. of California, Santa Cruz (United States)
Darren M. Fitzpatrick, Univ. of California, Santa Cruz (United States)
David P. Helmbold, Univ. of California, Santa Cruz (United States)

Published in SPIE Proceedings Vol. 6499:
Vision Geometry XV
Longin Jan Latecki; David M. Mount; Angela Y. Wu, Editor(s)

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