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dc.contributor.authorChakraborty, B.
dc.contributor.authorKaustubha, R.
dc.contributor.authorHegde, Amay
dc.contributor.authorPereira, A.
dc.date.accessioned2009-01-07T10:28:49Z
dc.date.available2009-01-07T10:28:49Z
dc.date.issued2001
dc.identifier.citationIEEE Transactions in Geoscience and Remote Sensing, Vol.39; 2722-2725p.
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/1548
dc.description.abstractA self-organizing feature map (SOFM), a kind of artificial neural network (ANN) architecture, is used in this work for continental shelf seafloor sediment classification. Echo data are acquired using an echosounding system from three types of seafloor sediment areas. Excellent classification (approx. 100%) for an ideal output neuron grid size of 15 x 1 is obtained for a moving average of 35 input snapshots
dc.language.isoen
dc.publisherIEEE
dc.rightsCopyright [2001]. All efforts have been made to respect the copyright to the best of our knowledge. Inadvertent omissions, if brought to our notice, stand for correction and withdrawal of document from this repository.
dc.subjectacoustic equipment
dc.subjectechosounders
dc.subjectseafloor mapping
dc.subjectsediments
dc.subjectechosounding
dc.subjectbackscatter
dc.titleAcoustic seafloor sediment classification using self-organizing feature maps
dc.typeJournal Article


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