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ICES Journal of Marine Science: Journal du Conseil Advance Access originally published online on February 2, 2009
ICES Journal of Marine Science: Journal du Conseil 2009 66(6):1130-1135; doi:10.1093/icesjms/fsp004
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© 2009 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

This article appears in the following ICES Journal of Marine Science issue: The Ecosystem Approach with Fisheries Acoustics and Complementary Technologies [View the issue table of contents]

Kernel methods for the detection and classification of fish schools in single-beam and multibeam acoustic data

Bart Buelens1, Tim Pauly1, Raymond Williams2 and Arthur Sale2

1 Myriax Software Pty Ltd, GPO Box 1387, Hobart, TAS 7001, Australia
2 School of Computing and Information Systems, University of Tasmania, Private Bag 100, Hobart, TAS 7001, Australia

Correspondence to B. Buelens: tel: +61 3 62315588; fax: +61 3 62341822; e-mail: bart.buelens{at}myriax.com

Buelens, B., Pauly, T., Williams, R., and Sale, A. 2009. Kernel methods for the detection and classification of fish schools in single-beam and multibeam acoustic data. – ICES Journal of Marine Science, 66: 1130–1135.

A kernel method for clustering acoustic data from single-beam echosounder and multibeam sonar is presented. The algorithm is used to detect fish schools and to classify acoustic data into clusters of similar acoustic properties. In a preprocessing routine, data from single-beam echosounder and multibeam sonar are transformed into an abstracted representation by multidimensional nodes, which are datapoints with spatial, temporal, and acoustic features as components. Kernel methods combine these components to determine clusters based on joint spatial, temporal, and acoustic similarities. These clusters yield a classification of the data in groups of similar nodes. Including the spatial components results in clusters for each school and effectively detects fish schools. Ignoring the spatial components yields a classification according to acoustic similarities, corresponding to classes of different species or age groups. The method is described and two case studies are presented.

Keywords: classification, detection of fish schools, kernel methods, multibeam sonar

Received 5 August 2008; accepted 24 October 2008; advance access publication 2 February 2009.


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