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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 1, JANUARY 2004 189 Comparison of Different Classification Algorithms for Underwater
 

Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 15, NO. 1, JANUARY 2004 189
Comparison of Different Classification Algorithms for Underwater
Target Discrimination
Donghui Li, Mahmood R. Azimi-Sadjadi, and Marc Robinson
Abstract--Classification of underwater targets from the acoustic
backscattered signals is considered here. Several different classifi-
cation algorithms are tested and benchmarked not only for their
performance but also to gain insight to the properties of the fea-
ture 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.
Index Terms--K-nearest neighbor (K-NN) classifier, neural net-
works, probabilistic neural networks (PNNs), support vector ma-
chines (SVMs), underwater target classification.
I. INTRODUCTION
THE problem of classifying underwater targets from the
acoustic backscattered signals involves discrimination be-
tween targets and nontarget objects as well as the characteriza-
tion of background clutter. Several factors that complicate this

  

Source: Azimi-Sadjadi, Mahmood R. - Department of Electrical and Computer Engineering, Colorado State University

 

Collections: Computer Technologies and Information Sciences