Multifrequency species classification of acoustic-trawl survey data using semi-supervised learning with class discovery

Published on Apr 1, 2011in Journal of the Acoustical Society of America1.819
· DOI :10.1121/1.3678685
Mathieu Woillez12
Estimated H-index: 12
Patrick H. Ressler17
Estimated H-index: 17
+ 1 AuthorsJohn K. Horne25
Estimated H-index: 25
Acoustic surveys often use multifrequency backscatter to estimate fish and plankton abundance. Direct samples are used to validate species classification of acoustic backscatter, but samples may be sparse or unavailable. A generalized Gaussian mixture model was developed to classify multifrequency acoustic backscatter when not all species classes are known. The classification, based on semi-supervised learning with class discovery, was applied to data collected in the eastern Bering Sea during summers 2004, 2007, and 2008. Walleye pollock, euphausiids, and two other major classes occurring in the upper water column were identified.
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