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Proceedings Paper

Comparison of approaches to classifier fusion for improving mine detection/classification performance
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 from their application. In particular, drawing on the Machine Learning literature, we contrast here results obtained from employing the bagging and boosting methods for classifier fusion, in the attempt to obtain more desirable performance characteristics than that achieved with single classifiers.

Paper Details

Date Published: 13 August 2002
PDF: 9 pages
Proc. SPIE 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, (13 August 2002); doi: 10.1117/12.479115
Show Author Affiliations
Martin G. Bello, Alphatech Inc. (United States)


Published in SPIE Proceedings Vol. 4742:
Detection and Remediation Technologies for Mines and Minelike Targets VII
J. Thomas Broach; Russell S Harmon; Gerald J. Dobeck, Editor(s)

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