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Kernel methods for the detection and classification of fish schools in single-beam and multi beam acoustic data
journal contribution
posted on 2023-05-17, 01:44 authored by Buelens, B, Pauly, T, Williams, RN, Sale, AHJA 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.
History
Publication title
I C E S Journal of Marine ScienceVolume
66Issue
6Pagination
1130-1135ISSN
1054-3139Department/School
School of Information and Communication TechnologyPublisher
Academic Press Ltd Elsevier Science LtdPlace of publication
24-28 Oval Rd, London, England, Nw1 7DxRights statement
The definitive publisher-authenticated version is available online at: www.oxfordjournals.org Copyright © 2009 Oxford University PressRepository Status
- Restricted