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Proceedings Paper

Classification of underwater mine-like and non-mine-like objects using canonical correlations
Author(s): Ali Pezeshki; Mahmood R. Azimi-Sadjadi; Louis L. Scharf
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Paper Abstract

A feature extraction method for underwater target classification is developed that exploits the linear dependence (coherence) between two sonar returns. A canonical coordinate decomposition is applied to resolve two consecutive acoustic backscattered signals into their dominant canonical coordinates. The corresponding canonical correlations are selected as features for classifying mine-like from non-mine-like objects. Test results are based on a subset of a wideband data set that has been collected at the Applied Research Lab (ARL), University of Texas (UT)-Austin. This subset includes returns from different mine-like and non-mine-like objects at several aspect angles in a smooth bottom condition. The test results demonstrate the potential of the canonical correlation-based feature extraction for underwater target classification and indicate that canonical correlation features are indeed robust to variations in aspect angle.

Paper Details

Date Published: 21 September 2004
PDF: 6 pages
Proc. SPIE 5415, Detection and Remediation Technologies for Mines and Minelike Targets IX, (21 September 2004); doi: 10.1117/12.543169
Show Author Affiliations
Ali Pezeshki, Colorado State Univ. (United States)
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)
Louis L. Scharf, Colorado State Univ. (United States)

Published in SPIE Proceedings Vol. 5415:
Detection and Remediation Technologies for Mines and Minelike Targets IX
Russell S. Harmon; J. Thomas Broach; John H. Holloway Jr., Editor(s)

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