Buried Underwater Object Classification Using a Collaborative Multi-Aspect Classifier

J. Cartmill, M. Azimi-Sadjadi, N. Wachowski
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引用次数: 15

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.
基于协同多面向分类器的水下目标分类
本文介绍了一种新的协同多面向分类系统(CMAC)。CMAC利用一组协作决策代理,能够根据多个方面获得的特征产生高置信度的最终决策。然后将该系统应用于水下目标分类问题。结果表明,与多ping决策级融合分类器相比,CMAC对类地雷目标提供了出色的多ping分类,同时减少了误报数量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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