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Proceedings Paper

Robust real-time mine classification based on side-scan sonar imagery
Author(s): Martin G. Bello
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Paper Abstract

We describe here image processing and neural network based algorithms for detection and classification of mines in side-scan sonar imagery, and the results obtained from their application to two distinct image data bases. These algorithms evolved over a period from 1994 to the present, originally at Draper Laboratory, and currently at Alphatech Inc. The mine-detection/classification system is partitioned into an anomaly screening stage followed by a classification stage involving the calculation of features on blobs, and their input into a multilayer perceptron neural network. Particular attention is given to the selection of algorithm parameters, and training data, in order to optimize performance over the aggregate data set.

Paper Details

Date Published: 22 August 2000
PDF: 7 pages
Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000); doi: 10.1117/12.396265
Show Author Affiliations
Martin G. Bello, Alphatech Inc. (United States)


Published in SPIE Proceedings Vol. 4038:
Detection and Remediation Technologies for Mines and Minelike Targets V
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Regina E. Dugan, Editor(s)

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