Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/7211
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.creator | Tang, QH | - |
dc.creator | Liu, BH | - |
dc.creator | Chen, YQ | - |
dc.creator | Zhou, XH | - |
dc.creator | Ding, JS | - |
dc.date.accessioned | 2015-11-10T08:32:37Z | - |
dc.date.available | 2015-11-10T08:32:37Z | - |
dc.identifier.issn | 1000-3630 | - |
dc.identifier.uri | http://hdl.handle.net/10397/7211 | - |
dc.language.iso | zh | en_US |
dc.publisher | 中国学术期刊(光盘版)电子杂志社 | en_US |
dc.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。 | en_US |
dc.rights | © 2007 China Academic Journal Electronic Publishing House. It is to be used strictly for educational and research use. | en_US |
dc.subject | SOM neural network | en_US |
dc.subject | Multibeam sonar systems | en_US |
dc.subject | Acoustic seafloor classification | en_US |
dc.subject | Backscatter Strength | en_US |
dc.title | 基于自组织神经网络的声学底质分类研究 | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.description.otherinformation | Author name used in this publication: 唐秋华 | en_US |
dc.description.otherinformation | Author name used in this publication: 刘保华 | en_US |
dc.description.otherinformation | Author name used in this publication: 陈永奇 | en_US |
dc.description.otherinformation | Author name used in this publication: 周兴华 | en_US |
dc.description.otherinformation | Author name used in this publication: 丁继胜 | en_US |
dc.description.otherinformation | Title in Traditional Chinese: 基于自組織神經網絡的聲學底質分類研究 | en_US |
dc.description.otherinformation | Journal title in Traditional Chinese: 聲學技術 | en_US |
dc.identifier.spage | 380 | - |
dc.identifier.epage | 384 | - |
dc.identifier.volume | 26 | - |
dc.identifier.issue | 3 | - |
dcterms.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。 | - |
dcterms.abstract | 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. | - |
dcterms.accessRights | open access | en_US |
dcterms.alternative | Acoustic seafloor classification using self-organizing map neural network | - |
dcterms.bibliographicCitation | 声学技术 (Technical acoustics), June 2007, v. 26, no. 3, p. 380-384 | - |
dcterms.isPartOf | 声学技术 (Technical acoustics) | - |
dcterms.issued | 2007 | - |
dc.identifier.rosgroupid | r38008 | - |
dc.description.ros | 2007-2008 > Academic research: refereed > Publication in refereed journal | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Tang_Acoustic_Seafloor_Classification.pdf | 523.11 kB | Adobe PDF | View/Open |
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