Seabed Classification Based on SOFM Neural Network

descriptionPublicationkeyboard_double_arrow_right Article , Conference object 01 Jan 2008Publisher:IEEEJournal:2008 International Conference on Computer Science and Software Engineering

Authors: Jianhu Zhao; Hongmei Zhang 0002;

doi: 10.1109/csse.2008.599

Seabed Classification Based on SOFM Neural Network

- Summary
- Metrics

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.

Related Organizations

Wuhan University
China (People's Republic of)

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	2
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average