Title :
Seabed Classification Based on SOFM Neural Network
Author :
Zhao, Jianhu ; Zhang, Hongmei
Author_Institution :
Sch. of Geodesy & Geomatics, Wuhan Univ., Wuhan
Abstract :
The multibeam bathymetric system (MBS) can provide both sounding data and acoustic backscattering strength data. Using the latter, Seabed classification can be fulfilled. This paper presents a method of seabed classification based on self-organizing feature maps (SOFM) neural network. The complete theory and data processing procedure are researched in detail. Some valuable conclusions are drawn through experiments and analysis. Several experiments proved this method and these conclusions.
Keywords :
acoustic wave scattering; bathymetry; geophysics computing; image classification; radar imaging; self-organising feature maps; sonar; SOFM neural network; acoustic backscattering strength data; multibeam bathymetric system; seabed classification; self-organizing feature maps; sounding data; Acoustic beams; Backscatter; Filtering; Image sampling; Neural networks; Propagation losses; Sea floor; Sonar; Spectral analysis; Structural beams; multibeam bathymetric system (MBS); neural network; seabed classification; sonar image;
Conference_Titel :
Computer Science and Software Engineering, 2008 International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-0-7695-3336-0
DOI :
10.1109/CSSE.2008.599
