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Proceedings Paper

Coherent-based method for detection of underwater objects from sonar imagery
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Paper Abstract

Detection and classification of underwater objects in sonar imagery are challenging problems. In this paper, a new coherent-based method for detecting potential targets in high-resolution sonar imagery is developed using canonical correlation analysis (CCA). Canonical coordinate decomposition allows us to quantify the changes between the returns from the bottom and any target activity in sonar images and at the same time extract useful features for subsequent classification without the need to perform separate detection and feature extraction. Moreover, in situations where any visual analysis or verification by human operators is required, the detected/classified objects can be reconstructed from the coherent features. In this paper, underwater target detection using the canonical correlations extracted from regions of interest within the sonar image is considered. Test results of the proposed method on underwater side-scan sonar images provided by the Naval Surface Warfare Center (NSWC) in Panama City, FL is presented. This database contains synthesized targets in real background varying in degree of difficulty and bottom clutter. Results illustrating the effectiveness of the CCA based detection method are presented in terms of probability of detection, and false alarm rates for various densities of background clutter.

Paper Details

Date Published: 26 April 2007
PDF: 8 pages
Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 65530U (26 April 2007); doi: 10.1117/12.718837
Show Author Affiliations
James D. Tucker, Colorado State Univ. (United States)
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)
Gerry J. Dobeck, Naval Surface Warfare Ctr. Panama City (United States)


Published in SPIE Proceedings Vol. 6553:
Detection and Remediation Technologies for Mines and Minelike Targets XII
Russell S. Harmon; J. Thomas Broach; John H. Holloway, Editor(s)

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