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dc.contributor.authorChakraborty, B.
dc.contributor.authorMahale, V.
dc.contributor.authorDeSouza, C.
dc.contributor.authorDas, P.
dc.date.accessioned2008-07-02T05:06:16Z
dc.date.available2008-07-02T05:06:16Z
dc.date.issued2004
dc.identifier.citationIEEE Geoscience And Remote Sensing Letters, Vol.1; 196-200p.
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/1146
dc.description.abstractThis letter presents seafloor classification study results of a hybrid artificial neural network architecture known as learning vector quantization. Single beam echo-sounding backscatter waveform data from three different seafloors of the western continental shelf of India are utilized. In this letter, an analysis is presented to establish the hybrid network as an efficient alternative for real-time seafloor classification of the acoustic backscatter data.
dc.language.isoen
dc.publisherIEEE
dc.rightsCopyright [2004]. 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 If it is brought to our notice that the rights are violated then the item would be withdrawn.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.subjectocean floor
dc.subjectseafloor mapping
dc.subjectbackscatter
dc.subjectacoustic data
dc.subjectcontinental shelves
dc.titleSeafloor classification using echo- waveforms: A method employing hybrid neural network architecture
dc.typeJournal Article


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