Classification using a radial basis function neural network on side-scan sonar data

Skinner, D.; Foo, S.Y.

doi:/10.1109/isie.2007.4374879

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Classification using a radial basis function neural network on side-scan sonar data

Skinner, D.; Foo, S.Y. (2007). Classification using a radial basis function neural network on side-scan sonar data, in: 2007 IEEE International Symposium on Industrial Electronics: Proceedings 4-7 June 2007, Vigo, Spain. pp. 1803-1806. https://dx.doi.org/10.1109/isie.2007.4374879

In: (2007). 2007 IEEE International Symposium on Industrial Electronics: Proceedings 4-7 June 2007, Vigo, Spain. IEEE: [Piscataway, New Jersey]. ISBN 978-1-4244-0754-5. https://dx.doi.org/10.1109/ISIE10556.2007, more

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Document type: Conference paper

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Skinner, D. Foo, S.Y.

Abstract

Detecting and classifying mines among natural formations and man-made debris along the sea floor can be a tedious task. To reduce operator dependency, an automated computer aided detection and classification system is needed. Our proposed automated system uses a two-step process. First the images are normalized and then a supervised learning method, radial basis function neural network (RBFNN), is applied to a side-scan sonar (SSS) data set. This method is able to extrapolate beyond the training data and successfully classify mine-like objects (MLOs).

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