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Buried Underwater Object Classification Using a Collaborative Multiaspect Classifier
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AI
DOI：10.1109/JOE.2008.2008041.png)
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Abstract
En 中文
In this paper, a new collaborative multiaspect classification system (CMAC) is introduced, which utilizes a group of collaborative decision-making agents capable of producing a high-confidence final decision based on features obtained over multiple aspects. It is also shown how CMAC can be modified to perform multiaspect classification using a decision feedback (DF) strategy. The system is then applied to a buried underwater target classification problem. The results show that CMAC provides excellent multiple-ping classification of mine-like objects while reducing the number of false alarms compared to other multiple-ping classification fusion systems such as nonlinear decision-level fusion (DLF).
Keywords:
Bayes classification
buried object scanning sonar system
collaborative decision making
underwater target classification
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