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Proceedings Paper

Comparison of two classifier training methodologies for underwater mine detection/classification
Author(s): Martin G. Bello
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Paper Abstract

We describe here the current form of Alphatech's image processing and neural network based algorithms for detection and classification of mines in side-scan sonar imagery, and results obtained form their application to three distinct databases. In particular, we contrast here results obtained from the use of a currently employed 'baseline' multilayer perceptron classifier training approach, with the use of a state of the art commercial neural network package, NeuralSIM, developed by Neuralware, Inc.

Paper Details

Date Published: 18 October 2001
PDF: 14 pages
Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001); doi: 10.1117/12.445441
Show Author Affiliations
Martin G. Bello, Alphatech Inc. (United States)

Published in SPIE Proceedings Vol. 4394:
Detection and Remediation Technologies for Mines and Minelike Targets VI
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Vivian George, Editor(s)

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