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Summary: Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA, August 12-17, 2007
Buried Underwater Object Classification Using a Collaborative
Multi-Aspect Classifier
Jered Cartmill, Mahmood R. Azimi-Sadjadi, and Neil Wachowski
Abstract- In this paper, a new collaborative multi-aspect
classification system (CMAC) is introduced. CMAC utilizes
a group of collaborative decision-making agents capable of
producing a high-confidence final decision based on features
obtained over multiple aspects. This system is then applied to
a buried underwater target classification problem. The results
show that CMAC provides excellent multi-ping classification
of mine-like objects while simultaneously reducing the number
of false alarms compared to a multi-ping decision-level fusion
classifier.
I. INTRODUCTION
Classification of buried underwater objects is challenging
owing to several issues that include: variability of target
signatures and features with respect to the incidence angle
and range ofthe sonar, the presence of competing natural and
man-made clutter, surface and bottom reverberation effects,
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