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dc.contributor.authorChakraborty, B.
dc.contributor.authorKodagali, V.N.
dc.contributor.authorBaracho, J.
dc.date.accessioned2008-07-18T05:45:15Z
dc.date.available2008-07-18T05:45:15Z
dc.date.issued2003
dc.identifier.citationIEEE Journal of Oceanic Engineering, Vol.28; 121-128p.
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/1246
dc.description.abstractThe presently studied numerical model, e.g., composite roughness, is successful for the purpose of sea-floor classification employing processed multibeam angular backscatter data from manganese-nodule-bearing locations of the Central Indian Ocean Basin. Hybrid artificial neural network (ANN) architecture, comprised of the self-organizing feature map and learning vector quantization (LVQ), has been implemented as an alternative technique for sea-floor roughness classification, giving comparative results with the aforesaid numerical model for processed multibeam angular backscatter data. However, the composite-roughness model approach is protracted due to the inherent need for processed data including system-gain corrections. In order to establish that tedious processing of raw backscatter values is unessential for efficient classification, hybrid ANN architecture has been attempted here due to its nonparametric approach. In this technical communication, successful employment of LVQ algorithm for unprocessed (raw) multibeam backscatter data indicates true real-time classification application
dc.language.isoen
dc.publisherIEEE
dc.rightsCopyright [2003]. It is tried to respect the rights of the copyright holders to the best of the knowledge. If it is brought to our notice that the rights are violated then the item would be withdrawn.
dc.subjectseafloor mapping
dc.subjectmultibeam sonar
dc.subjectechosounding
dc.subjectechosounders
dc.subjectbackscatter
dc.subjectmathematical models
dc.titleSea-floor classification using multibeam echo-sounding angular backscatter data: A real-time approach employing hybrid neural network architecture
dc.typeJournal Article


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