Classification of buried underwater objects using the new BOSS and multichannel canonical correlation feature extraction

Jered Cartmill; Bryan Thompson; Mahmood R. Azimi-Sadjadi

doi:10.1117/12.666924

18 May 2006 Classification of buried underwater objects using the new BOSS and multichannel canonical correlation feature extraction

Jered Cartmill, Bryan Thompson, Mahmood R. Azimi-Sadjadi

Author Affiliations +

Proceedings Volume 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI; 62171F (2006) https://doi.org/10.1117/12.666924
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

Jered Cartmill, Bryan Thompson, and Mahmood R. Azimi-Sadjadi "Classification of buried underwater objects using the new BOSS and multichannel canonical correlation feature extraction", Proc. SPIE 6217, Detection and Remediation Technologies for Mines and Minelike Targets XI, 62171F (18 May 2006); https://doi.org/10.1117/12.666924

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