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22 August 2000 Robust real-time mine classification based on side-scan sonar imagery
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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.
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Martin G. Bello "Robust real-time mine classification based on side-scan sonar imagery", Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets V, (22 August 2000);

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