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Proceedings Paper

Graph segmentation and support vector machines for bare earth classification from lidar
Author(s): Nicholas S. Shorter; O'Neil Smith; Philip Smith; Mark Rahmes
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Paper Abstract

A novel approach using a support vector machine (SVM) is proposed to classify bare earth points in LiDAR point clouds. Using graph based segmentation, the LiDAR point cloud is segmented into a set of topological components. Several features establishing relationships from those components to their neighboring components are formulated. The SVM is then trained on the segment features to establish a model for the classification of bare earth and non bare earth points. Quantitative results are presented for training and testing the proposed SVM classifier on the ISPRS data set. Using the ISPRS data set as a training set, qualitative results are presented by testing the proposed SVM classifier on data downloaded from Open Topography; which covers a variety of different landscapes and building structures in Frazier Park, California. Despite the data being captured from different sensors, and collected from scenes with different terrain types and building structures, the results shown were processed with no parameter changes. Furthermore, a confidence value is returned indicating how well the unforeseen data fits the SVM’s trained model for bare earth recognition.

Paper Details

Date Published: 9 June 2014
PDF: 9 pages
Proc. SPIE 9080, Laser Radar Technology and Applications XIX; and Atmospheric Propagation XI, 90800Q (9 June 2014); doi: 10.1117/12.2050439
Show Author Affiliations
Nicholas S. Shorter, Harris Corp. (United States)
O'Neil Smith, Harris Corp. (United States)
Philip Smith, Harris Corp. (United States)
Mark Rahmes, Harris Corp. (United States)


Published in SPIE Proceedings Vol. 9080:
Laser Radar Technology and Applications XIX; and Atmospheric Propagation XI
Monte D. Turner; Linda M. Wasiczko Thomas; Gary W. Kamerman; Earl J. Spillar, Editor(s)

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