Comparison of two classifier training methodologies for underwater mine detection/classification

Martin G. Bello

doi:10.1117/12.445441

18 October 2001 Comparison of two classifier training methodologies for underwater mine detection/classification

Martin G. Bello

Proceedings Volume 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI; (2001) https://doi.org/10.1117/12.445441
Event: Aerospace/Defense Sensing, Simulation, and Controls, 2001, Orlando, FL, United States

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.

Citation Download Citation

Martin G. Bello "Comparison of two classifier training methodologies for underwater mine detection/classification", Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001); https://doi.org/10.1117/12.445441

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