한국수산과학회지 (Korean Journal of Fisheries and Aquatic Sciences)

  • 제51권3호
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  • Pages.321-327
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  • 2018
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  • 0374-8111(pISSN)
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  • 2287-8815(eISSN)
한국수산과학회 (The Korean Society of Fisheries and Aquatic Science)
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확률 신경망에 의한 해저 저질의 식별

Classifying Seafloor Sediments Using a Probabilistic Neural Network

  • 이대재 (부경대학교 해양생산시스템관리학부)
  • Lee, Dae-Jae (Division of Marine Production System Management, Pukyong National University)
  • 투고 : 2018.05.02
  • 심사 : 2018.05.16
  • 발행 : 2018.06.30
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초록

To classify seafloor sediments using a probabilistic neural network (PNN), the frequency-dependent characteristics of broadband acoustic scattering, which make it possible to qualitatively categorize seabed type, were collected from three different geographical areas in Korea. The echo data samples from three types of seafloor sediment were measured using a chirp sonar system operating over a frequency range of 20-220 kHz. The spectrum amplitudes for frequency responses of 35-75 kHz were fed into the PNN as input feature parameters. The PNN algorithm could successfully identify three seabed types: mud, mud/shell and concrete sediments. The percentage probabilities of the three seabed types being correctly classified were 86% for mud, 66% for mud/shell and 72% for concrete sediment.

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