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