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    Fine scale analyses of a coralline bank mapped using multi-beam backscatter data

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    Proc_Symp_Underwater_Technol_UT15-121.pdf (1.488Mb)
    Date
    2015
    Author
    Menezes, A.A.A.
    Naik, M.
    Fernandes, W.A.
    Haris, K.
    Chakraborty, B.
    Estiberio, S.
    Lohani, R.B.
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    Abstract
    In this work, we have developed a classification technique to characterize the seafloor of the Gaveshani (coralline) bank area using multi-beam backscatter data. Softcomputational techniques like the artificial neural networks (ANNs) based unsupervised self-organizing maps (SOM) architecture is used to determine the existence of six classes. Thereafter, 55 segments were identified for data segmentation, employing six profiles selected from the backscatter maps, using the fuzzy c-means (FCM) algorithms. The gridded map of the segmented seafloor backscatter data was overlaid on the coregistered backscatter map to understand the distribution of the overlying sediment material on the bank
    URI
    http://drs.nio.org/drs/handle/2264/4729
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