Comparison of approaches to classifier fusion for improving mine detection/classification performance

Martin G. Bello

doi:10.1117/12.479115

13 August 2002 Comparison of approaches to classifier fusion for improving mine detection/classification performance

Martin G. Bello

Proceedings Volume 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII; (2002) https://doi.org/10.1117/12.479115
Event: AeroSense 2002, 2002, 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 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.

Citation Download Citation

Martin G. Bello "Comparison of approaches to classifier fusion for improving mine detection/classification performance", Proc. SPIE 4742, Detection and Remediation Technologies for Mines and Minelike Targets VII, (13 August 2002); https://doi.org/10.1117/12.479115

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