NDLI: Classification of fish schools based on evaluation of acoustic descriptor characteristics

Content Provider	Springer Nature Link
Author	Charef, Aymen Ohshimo, Seiji Aoki, Ichiro Al Absi, Natheer
Copyright Year	2009
Abstract	Acoustic surveys were conducted from 2002 to 2006 in the East China Sea off the Japanese coast in order to develop a quantitative classification typology of a pelagic fish community and other co-occurring fishes based on acoustic descriptors. Acoustic data were postprocessed to detect and extract fish aggregations from echograms. Based on the expert visual examination of the echograms, detected schools were divided into three broad fish groups according to their schooling characteristics and ethological properties. Each fish school was described by a set of associated descriptors in order to objectively allocate each echo trace to its fish group. Two methods of supervised classification were employed, the discriminant function analysis (DFA) and the artificial neural network technique (ANN). We evaluated and compared the performance of both methods, which showed encouraging and about equally highly correct classification rates (ANN 87.6%; DFA 85.1%). In both techniques, positional and then morphological parameters were most important in discriminating among fish schools. Fish catch composition from midwater trawling validated the fish group classification through one representative example of each grouping. Both methods provided the essential information required for assessing fish stocks. Similar techniques of fish classification might be applicable to marine ecosystems with high pelagic fish diversity.
Starting Page	1
Ending Page	11
Page Count	11
File Format	PDF
ISSN	09199268
Journal	Fisheries Science
Volume Number	76
Issue Number	1
e-ISSN	14442906
Language	English
Publisher	Springer Japan
Publisher Date	2009-12-08
Publisher Place	Japan
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Acoustic descriptor Artificial neural network Discriminant function analysis Fish classification Species identification Food Science Freshwater & Marine Ecology Fish & Wildlife Biology & Management
Content Type	Text
Resource Type	Article
Subject	Aquatic Science

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