Title
Broadband fish identification of Laurentian Great Lakes fishes
Abstract
Broadband acoustic echoes were collected on free-swimming alewives Alosa psuedoharungus, rainbow smelt Osmerus mordax, and bloaters Coregonus hoyi. Concurrent midwater trawls were used to determine species composition. A genetic neural network was trained on broadband echoes from each species to an overall correct classification rate of 91%. Tests of the trained network suggested an overall expected correct classification rate of 80-85% and indicated rainbow smelt and alewife echoes were less likely confused as compared to bloater. Application of the trained network to recorded echo data resulted in predicted species compositions that did not correspond well to those observed in the trawls. The classifiers may have been confounded by inclusion of echoes from various species, especially for bloater. A restriction to echoes collected concurrent to trawls or other controlled situations (e.g., pens or smaller lakes with single species) may be needed for building 'clean' classifiers.
Year
DOI
Venue
2004
10.1109/IGARSS.2004.1368688
Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings. 2004 IEEE International
Keywords
Field
DocType
aquaculture,data acquisition,echo,lakes,neural nets,underwater sound,AD 1998 to 2001,Laurentian Great Lakes fish identification,alewife Alosa psuedoharungus,bloaters Coregonus hoyi,broadband acoustic echo,concurrent midwater trawl,genetic neural network,rainbow smelt Osmerus mordax
Aquaculture,Coregonus hoyi,Computer science,Osmerus,Oceanography,Alosa,Rainbow smelt,Classification rate,Alewife
Conference
Volume
ISSN
ISBN
2
2153-6996
0-7803-8742-2
Citations 
PageRank 
References 
1
0.36
3
Authors
4
Name
Order
Citations
PageRank
Eric O. Rogers110.36
Guy W. Fleischer210.36
Patrick K. Simpson3142.56
Gerald F. Denny410.36