Peer-Reviewed Journal Details
Mandatory Fields
Marsh, I;Brown, C
2009
October
Applied Acoustics
Neural network classification of multibeam backscatter and bathymetry data from Stanton Bank (Area IV)
Published
WOS: 41 ()
Optional Fields
SIDESCAN SONAR IMAGERY SCOTIAN SHELF CONTINENTAL-SHELF SCAN SONAR SEA CALIFORNIA GEOLOGY MORPHOLOGY SEDIMENTS TEXTURE
70
1269
1276
The paper presents an approach to automated seabed classification that incorporates spatially coincident bathymetric and backscatter data collected in multibeam surveys. The classification algorithm is a self-organising artificial neural network that can be used as a rapid classifier of grids of bathymetry (and attributes such as slope and roughness) and backscatter strength (and textures), or in a mode that uses both datasets at beam level to construct high spatial resolution classifications that preserve angular information in the backscatter. The latter mode requires processing of backscatter angular responses in a manner consistent with the essential physics of acoustic scattering from the seafloor. (C) 2008 Published by Elsevier Ltd.
0003-682X
10.1016/j.apacoust.2008.07.012
Grant Details
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