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    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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    Proc_COT_03_293.pdf (1.031Mb)
    Date
    2003
    Author
    Chakraborty, B.
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    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.
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    http://drs.nio.org/drs/handle/2264/1249
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