Record Details

Acoustic seafloor sediment classification using self-organizing feature maps

DRS at CSIR-National Institute of Oceanography

View Archive Info
 
 
Field Value
 
Title Acoustic seafloor sediment classification using self-organizing feature maps
 
Creator Chakraborty, B.
Kaustubha, R.
Hegde, A.
Pereira, A.
 
Subject acoustic equipment
echosounders
seafloor mapping
sediments
echosounding
backscatter
 
Description 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
 
Date 2009-01-07T10:28:49Z
2009-01-07T10:28:49Z
2001
 
Type Journal Article
 
Identifier IEEE Transactions in Geoscience and Remote Sensing, Vol.39; 2722-2725p.
http://drs.nio.org/drs/handle/2264/1548
 
Language en
 
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.
 
Publisher IEEE