Share Email Print
cover

Proceedings Paper

Automated detection and classification of sea mines in sonar imagery
Author(s): Gerald J. Dobeck; John C. Hyland; Le'Derick Smedley
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 22 July 1997
PDF: 21 pages
Proc. SPIE 3079, Detection and Remediation Technologies for Mines and Minelike Targets II, (22 July 1997); doi: 10.1117/12.280846
Show Author Affiliations
Gerald J. Dobeck, Naval Surface Warfare Ctr. (United States)
John C. Hyland, Naval Surface Warfare Ctr. (United States)
Le'Derick Smedley, Naval Surface Warfare Ctr. (United States)


Published in SPIE Proceedings Vol. 3079:
Detection and Remediation Technologies for Mines and Minelike Targets II
Abinash C. Dubey; Robert L. Barnard, Editor(s)

© SPIE. Terms of Use
Back to Top