Please use this identifier to cite or link to this item:
Title: 基于自组织神经网络的声学底质分类研究
Other Titles: Acoustic seafloor classification using self-organizing map neural network
Authors: Tang, QH
Liu, BH
Chen, YQ 
Zhou, XH
Ding, JS
Keywords: SOM neural network
Multibeam sonar systems
Acoustic seafloor classification
Backscatter Strength
Issue Date: 2007
Publisher: 中国学术期刊(光盘版)电子杂志社
Source: 声学技术 (Technical acoustics), June 2007, v. 26, no. 3, p. 380-384 How to cite?
Journal: 声学技术 (Technical acoustics) 
Abstract: 研究利用多波束测深系统获取的反向散射强度数据,应用自组织(Self Organizing Map,简称SOM)神经网络分类方法实现了对海底泥、砂、砾石和基岩等底质类型的快速、有效识别。通过实验示例,将SOM神经网络的分类结果与传统海底地质取样获取的真实底质类型进行分析比较,表明该方法是可行和有效的。
Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. Using the seafloor-backscattered data from each beam and with automatic classification, seabed sediments distribution maps can be obtained directly. In this paper, the self-organizing map (SOM) neural network is used in acoustic seafloor classification from multibeam sonar data. This method can rapidly identify all kinds of seafloor types such as mud, sand, gravel and rock in the experimental surveying areas. Compared with the traditional geologic grab method, the experiment indicates that the SOM method is feasible and valid.
ISSN: 1000-3630
Rights: © 2007 中国学术期刊电子杂志出版社。本内容的使用仅限于教育、科研之目的。
© 2007 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research use.
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Tang_Acoustic_Seafloor_Classification.pdf523.11 kBAdobe PDFView/Open
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

Last Week
Last month
Citations as of Aug 14, 2018


Citations as of Aug 14, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.