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Proceedings Paper

Unsupervised segmentaiton of subsurface radar images
Author(s): Waleed Al-Nuaimy; Yi Huang; S. Shihab; Asger Eriksen
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Paper Abstract

The volume of image data generated in ground-penetrating radar surveys can severely restrict the practicality of this site investigation technique. This is particularly true in situations where automatic analysis or interpretation is required, as segmentation and classification tasks that utilise multivariate data are critically affected by the volume and dimensionality of the data. A general-purpose unsupervised image segmentation system is presented here for the automatic detection of image regions exhibiting different visual texture properties. A suboptimal feature selection procedure is proposed to automatically select the set of texture features best suited for the particular application. The reduction in the size of the feature set both reduces the computation time and improves the accuracy of the final classification.

Paper Details

Date Published: 12 April 2002
PDF: 4 pages
Proc. SPIE 4758, Ninth International Conference on Ground Penetrating Radar, (12 April 2002); doi: 10.1117/12.462233
Show Author Affiliations
Waleed Al-Nuaimy, Univ. of Liverpool (United Kingdom)
Yi Huang, Univ. of Liverpool (United Kingdom)
S. Shihab, Univ. of Liverpool (United Kingdom)
Asger Eriksen, Zetica Ltd. (United Kingdom)

Published in SPIE Proceedings Vol. 4758:
Ninth International Conference on Ground Penetrating Radar
Steven Koppenjan; Hua Lee, Editor(s)

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