Quantitative seafloor characterization using angular backscatter data of the multi-beam echo-sounding system - Use of models and model free techniques

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dc.contributor.author Chakraborty, B.
dc.date.accessioned 2008-07-18T05:45:16Z
dc.date.available 2008-07-18T05:45:16Z
dc.date.issued 2003
dc.identifier.citation Proceedings of the International Conference on Coastal and Ocean Technology, December 10-12, 2003, 293-300p.
dc.identifier.uri http://drs.nio.org/drs/handle/2264/1249
dc.description.abstract For quantitative seafloor roughness characterization and classification using multi-beam processed backscatter data, a good correlation is indicated among the power law parameters (composite roughness model) and hybrid ANN architecture results. Moderate classification efficiencies are achieved in the range of 32% to 46% using the SOFM classifier. Implementation of a neural-based hybrid classifier uses SOFM as a precursor to broadly identify the number of classes in the input space. For further improved classification exercise of multi-beam backscatter data, supervised learning algorithms of LVQ1 and LVQ2 have been successfully adopted. Present study emphasizes that unprocessed (raw) backscatter data provides successful real-time classification of seafloor roughness using hybrid ANN.
dc.language.iso en
dc.publisher Allied, India
dc.rights Copyright [2003]. It is tried to respect the rights of the copyright holders to the best of the knowledge. If it is brought to our notice that the rights are violated then the item would be withdrawn.
dc.subject echosounders
dc.subject ocean floor
dc.subject roughness
dc.subject backscatter
dc.subject modelling
dc.subject oceanographic equipment
dc.title Quantitative seafloor characterization using angular backscatter data of the multi-beam echo-sounding system - Use of models and model free techniques
dc.type Conference Article


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