Seabed Classification Based on SOFM Neural Network

Author

Zhao, Jianhu ; Zhang, Hongmei

Author_Institution

Sch. of Geodesy & Geomatics, Wuhan Univ., Wuhan

Volume

4

fYear

2008

fDate

12-14 Dec. 2008

Firstpage

902

Lastpage

905

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Software Engineering, 2008 International Conference on

Conference_Location

Wuhan, Hubei

Print_ISBN

978-0-7695-3336-0

Type

conf

DOI

10.1109/CSSE.2008.599

Filename

4722764