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 If it is brought to our notice that the rights are violated then the item would be withdrawn.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 | |