Comparison of different classification algorithms for underwater target discrimination
Date
2004
Authors
Robinson, Marc, author
Azimi-Sadjadi, Mahmood R., author
Li, Donghui, author
IEEE, publisher
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Abstract
Classification of underwater targets from the acoustic backscattered signals is considered here. Several different classification algorithms are tested and benchmarked not only for their performance but also to gain insight to the properties of the feature space. Results on a wideband 80-kHz acoustic backscattered data set collected for six different objects are presented in terms of the receiver operating characteristic (ROC) and robustness of the classifiers wrt reverberation.
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Subject
probabilistic neural networks (PNNs)
neural networks
K-nearest neighbor (K-NN) classifier
support vector machines (SVMs)
underwater target classification