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Preference of echo features for classification of seafloor sediments using neural networks

DRS at CSIR-National Institute of Oceanography

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Title Preference of echo features for classification of seafloor sediments using neural networks
 
Creator De, C.
Chakraborty, B.
 
Subject ocean floor
echo ranging
acoustic equipment
neural networks
 
Description Selection of a set of dominant echo features to classify seafloor sediments using a multilayer perceptron neural network is investigated at two acoustic frequencies (33 and 210 kHz). Several sets of inputs with different combinations of two, three, four, five, and six echo features are exploited with three-layer neural networks. The performances of the networks are analyzed to assess the most discriminating set of echo features for classification of seafloor sediments. The results of the overall average performances reveal that backscatter strength and time spread are the two most important echo features at 33 kHz, whereas backscatter strength has higher discriminating characteristics at 210 kHz for seafloor sediment classification. In addition, a set of four echo features consisting of backscatter strength, time-spread, statistical skewness, and Hausdroff dimension gives the highest success at both the acoustic frequencies.
 
Date 2010-11-29T07:40:12Z
2010-11-29T07:40:12Z
2010
 
Type Journal Article
 
Identifier Marine Geophysical Researches, vol.31(3); 215-221
http://drs.nio.org/drs/handle/2264/3748
 
Language en
 
Rights An edited version of this paper was published by Springer. This paper is for R & D pupose and Copyright [2010] Springer.
 
Publisher Springer