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Proceedings Paper

Classification of buried underwater objects using the new BOSS and multichannel canonical correlation feature extraction
Author(s): Jered Cartmill; Bryan Thompson; Mahmood R. Azimi-Sadjadi
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Paper Abstract

Developing an effective detection and classification system for use with buried underwater objects is a challenging problem. In this paper, multichannel canonical correlation analysis (MCCA) is used for feature extraction from multiple sonar returns of buried underwater objects using data collected by the new generation Buried Object Scanning Sonar (BOSS) system. Comparisons are made between the classification results of features extracted by the proposed algorithm and those extracted by the two-channel canonical correlation analysis (CCA) algorithm on the SAX '04 data set. Extracted features are subsequently used in the development of classification systems able to differentiate between mine-like and non-mine-like objects. This study compares different feature extraction algorithms and classification schemes, and the results are presented in terms of classification rates and overall detection/classification performance. The results show that, for the SAX '04 data set, the features extracted via MCCA yield higher correct classification rates than feature extracted using CCA while simultaneously reducing structural complexity.

Paper Details

Date Published: 18 May 2006
PDF: 12 pages
Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 62171F (18 May 2006); doi: 10.1117/12.666924
Show Author Affiliations
Jered Cartmill, Colorado State Univ. (United States)
Bryan Thompson, Colorado State Univ. (United States)
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)

Published in SPIE Proceedings Vol. 6217:
Detection and Remediation Technologies for Mines and Minelike Targets XI
J. Thomas Broach; Russell S. Harmon; John H. Holloway Jr., Editor(s)

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