26 April 2007 Underwater target classification using the wing BOSS and multi-channel decision fusion
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In this paper, two different multi-aspect underwater target classification systems are evaluated based on their ability to correctly detect and classify mine-like objects. These methods are tested on a recently collected database that consists of sonar returns from various buried mine-like and non-mine-like objects in different operating and environmental conditions. In one approach, coherent features are extracted from the data using canonical correlation analysis (CCA) between two sonar pings. Classification is performed using a collaborative multi-aspect classifier (CMAC), which utilizes a group of collaborative decision-making agents capable of producing a high-confidence final decision based on these features. The second approach uses features generated by a multi-channel coherence analysis (MCA), which is an extension of CCA utilizing multiple sonar pings. The MCA features are then applied to a simple classifier. Results are presented in terms of correct classification rate and general detection and classification performance of each system in relation to the various operating and environmental conditions.
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Neil Wachowski, Neil Wachowski, Mahmood R. Azimi-Sadjadi, Mahmood R. Azimi-Sadjadi, Jered Cartmill, Jered Cartmill, } "Underwater target classification using the wing BOSS and multi-channel decision fusion", Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 65530Q (26 April 2007); doi: 10.1117/12.720931; https://doi.org/10.1117/12.720931

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