Comparison of different classification algorithms for underwater target discrimination

Author

Li, Donghui ; Azimi-Sadjadi, Mahmood R. ; Robinson, Marc

Author_Institution

Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA

Volume

15

Issue

1

fYear

2004

Firstpage

189

Lastpage

194

Abstract

Classification of underwater targets from the acoustic backscattered signals is considered. 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.

Keywords

acoustic signal processing; neural nets; pattern classification; signal classification; support vector machines; K-nearest neighbor classifier; SVMs; acoustic backscattered signals; classification algorithms; probabilistic neural networks; receiver operating characteristic; support vector machines; underwater target classification; underwater target discrimination; wideband 80-kHz acoustic backscattered data set; Acoustic testing; Benchmark testing; Cities and towns; Classification algorithms; Neural networks; Sea measurements; Support vector machine classification; Support vector machines; Underwater acoustics; Wideband; Acoustic Stimulation; Algorithms; Discrimination (Psychology); Normal Distribution;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2003.820621

Filename

1263590