Acoustic seafloor sediment classification using self-organizing feature maps

dc.contributor.author	Chakraborty, B.
dc.contributor.author	Kaustubha, R.
dc.contributor.author	Hegde, Amay
dc.contributor.author	Pereira, A.
dc.date.accessioned	2009-01-07T10:28:49Z
dc.date.available	2009-01-07T10:28:49Z
dc.date.issued	2001
dc.identifier.citation	IEEE Transactions in Geoscience and Remote Sensing, Vol.39; 2722-2725p.
dc.identifier.uri	http://drs.nio.org/drs/handle/2264/1548
dc.description.abstract	A 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.iso	en
dc.publisher	IEEE
dc.rights	Copyright [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.subject	acoustic equipment
dc.subject	echosounders
dc.subject	seafloor mapping
dc.subject	sediments
dc.subject	echosounding
dc.subject	backscatter
dc.title	Acoustic seafloor sediment classification using self-organizing feature maps
dc.type	Journal Article