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Proceedings Paper

Comparison of different neural network classification paradigms for underwater target discrimination
Author(s): Donghui Li; Mahmood R. Azimi-Sadjadi; Gerald J. Dobeck
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Paper Abstract

The problem of classification of underwater targets from the acoustic backscattered signals is considered in this paper. A wavelet packet-based feature extraction scheme is used in conjunction with the linear prediction coding scheme as the front-end-processor. Selected features with higher discriminatory power are then fed to a neural network classifier. Several different classification systems are benchmarked in this paper. These include K-nearest neighbor classifier, PNN and SVM. These paradigms are examined on the acoustic backscattered data for both 40 KHz and 80 KHz sonar bandwidth. Performance comparison of these systems with that of the previously used Back-Propagation Neural Network is provided as well.

Paper Details

Date Published: 22 August 2000
PDF: 12 pages
Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); doi: 10.1117/12.396261
Show Author Affiliations
Donghui Li, Colorado State Univ. (United States)
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)
Gerald J. Dobeck, Naval Surface Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 4038:
Detection and Remediation Technologies for Mines and Minelike Targets V
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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