Acoustic identification of marine species using a feature library

Sonars and echosounders are widely used for remote sensing of life in the marine environment. There is an ongoing need to make the acoustic identification of marine species more correct and objective and thereby reduce the uncertainty of acoustic abundance estimates. In our work, data from multi-fre...

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Bibliographic Details
Published in:Methods in oceanography (Oxford) 2016-12, Vol.17, p.187-205
Main Authors: Korneliussen, Rolf J., Heggelund, Yngve, Macaulay, Gavin J., Patel, Daniel, Johnsen, Espen, Eliassen, Inge K.
Format: Article
Language:English
Subjects:
Acoustic feature library
Acoustic-based categorization
Large Scale Survey System
LSSS
Relative frequency response
School-detection
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Summary:Sonars and echosounders are widely used for remote sensing of life in the marine environment. There is an ongoing need to make the acoustic identification of marine species more correct and objective and thereby reduce the uncertainty of acoustic abundance estimates. In our work, data from multi-frequency echosounders working simultaneously with nearly identical and overlapping acoustic beams are processed stepwise in a modular sequence to improve data, detect schools and categorize acoustic targets by means of the Large Scale Survey System software (LSSS). Categorization is based on the use of an acoustic feature library whose main components are the relative frequency responses. The results of the categorization are translated into acoustic abundance of species. The method is tested on acoustic data from the Barents Sea, the Norwegian Sea and the North Sea, where the target species were capelin (Mallotus villosus L.), Atlantic mackerel (Scomber scombrus L.) and sandeel (Ammodytes marinus L.), respectively. Manual categorization showed a high conformity with automatic categorization for all surveys, especially for schools. [Display omitted] •Echo data are categorized using an acoustic-feature library of ground-truthed target spectra.•Automatic data-processing modules allow faster scrutiny, better quality and objectivity of results.•Comparison of manual and automatic classification for three oceanic surveys in different ecosystems.•High correlation between manual and automatic classification of backscatter for all case studies.
ISSN:2211-1220
2211-1239
DOI:10.1016/j.mio.2016.09.002