22 July 1997 Automated detection and classification of sea mines in sonar imagery
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Abstract
An advanced capability for automated detection and classification of sea mines in sonar imagery has been developed. The advanced mine detection and classification (AMDAC) algorithm consists of an improved detection density algorithm, a classification feature extractor that uses a stepwise feature selection strategy, a k-nearest neighbor attractor-based neural network (KNN) classifier, and an optimal discriminatory filter classifier. The detection stage uses a nonlinear matched filter to identify mine-size regions in the sonar image that closely match a mine's signature. For each detected mine-like region, the feature extractor calculates a large set of candidate classification features. A stepwise feature selection process then determines the subset features that optimizes probability of detection and probability of classification for each of the classifiers while minimizing false alarms.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gerald J. Dobeck, Gerald J. Dobeck, John C. Hyland, John C. Hyland, Le'Derick Smedley, Le'Derick Smedley, } "Automated detection and classification of sea mines in sonar imagery", Proc. SPIE 3079, Detection and Remediation Technologies for Mines and Minelike Targets II, (22 July 1997); doi: 10.1117/12.280846; https://doi.org/10.1117/12.280846
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