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Proceedings Paper

Underwater target classification using multi-aspect fusion and neural networks
Author(s): Mahmood R. Azimi-Sadjadi; Qiang Huang; Gerald J. Dobeck
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Paper Abstract

This paper presents an extension of the research work on the wavelet-based classification scheme developed to discriminate underwater mine-like from non-mine-like objects using the acoustic backscattered signals. Based on the single-aspect classification results, the robustness and discriminatory power of the selected features, and the generalization ability of the trained network are demonstrated on several cases. To further improve the overall classification accuracy, the classification results of multiple aspect angles are fused together. Two different fusion approaches are considered and their performance is tested on ten different realizations. The final results show excellent classification accuracy of 96% for only a 4% false alarm rate.

Paper Details

Date Published: 4 September 1998
PDF: 8 pages
Proc. SPIE 3392, Detection and Remediation Technologies for Mines and Minelike Targets III, (4 September 1998); doi: 10.1117/12.324142
Show Author Affiliations
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)
Qiang Huang, Colorado State Univ. (United States)
Gerald J. Dobeck, Naval Surface Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 3392:
Detection and Remediation Technologies for Mines and Minelike Targets III
Abinash C. Dubey; James F. Harvey; J. Thomas Broach, Editor(s)

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