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Tytuł artykułu

Mapping seabed features from multibeam echosounder data using autocorrelation and multi-scale wavelet analyses

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Języki publikacji
EN
Abstrakty
EN
In the paper we propose the method of seabed morphological features extraction, which we have obtained from bathymetric and backscatter data, recorded by multibeam echosounder. Presented results of acoustical recognition of the southern Baltic Sea bottom are the part of measurements conducted in the band of 220 km length in the central part of Polish coastal water. The detailed analysis of seabed features were performed for area located in the vicinity of Kołobrzeg harbour. The degree of seafloor corrugation was determined by autocorrelation analysis of seafloor bathymetry. To which, we used estimation of autocorrelation length and fractal dimension, based on the shape of autocorrelation function. Moreover, the parameters of wavelet decomposition of bottom backscattering strength were the input to fuzzy logic clustering system allowing for outline of seafloor areas of similar morphological features. Both presented methods have confirmed its effectiveness in identifying morphological characteristics and types of the bottom surface.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
253--260
Opis fizyczny
Bibliogr. 13 poz., rys.
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autor
autor
autor
autor
autor
Bibliografia
  • [1] J. H. Clarke, Toward remote seafloor classification using the angular response of acoustic backscattering: A case study from multiple overlapping GLORIA data, IEEE Journal of Oceanic Engineering, Vol.19 (1), 112-127, 1994.
  • [2] B. Chakraborty, V. Kodagali, J. Baracho, Sea-floor classification using multibeam echo-sounding angular backscatter data: a real-time approach employing hybrid neural network architecture, IEEE Journal of Oceanic Engineering, Vol. 28 (1), 121–128, 2003.
  • [3] Z. Łubniewski, Using multibeam echoes in seafloor characterization and classification, Hydroacoustics, Vol. 11, 265-270, 2008.
  • [4] J. Tęgowski, N. Gorska, J. Nowak, A. Kruss, Analysis of single beam, multibeam and sidescan sonar data for benthic habitat classification in the southern Baltic Sea, Proceedings of 3rd International Conference and Exhibition on Underwater Acoustic Measurements: Technologies & Results, 21-26 July, 2009, Nafplion, Greece, Edited by: John S. Papadakis & Leif Bjorno, Volume I, II & III, ISBN: 978-960-98883-0-1, 978-960-98883-1-8, 978-960-98883-2-5, 978-960-98883-3-2, 131-138, 2009.
  • [5] Y. Rzhanov, G.R. Cutter, L.A Mayer, Seafloor segmentation based on bathymetric measurements from multibeam echosounder data, Proceedings of Seventh International Symposium on Signal Processing and its Applications, Vol. 1, 529-532, ISBN: 0-7803-7946-2, July, 2003.
  • [6] J.A Ogilvy, Theory of wave scattering from rough surfaces. Adam Hilger, 277, Bristol 1991.
  • [7] B.B. Mandelbrot, , The fractal geometry of nature, Freeman, 460, San Francisco,1982.
  • [8] T. Yamamoto, Acoustic scattering in the ocean from velocity and density fluctuations in the sediments, Journ. Acoust. Soc. Am., Vol. 99 (2), 866-879, 1996.
  • [9] J. Tęgowski, Z. Łubniewski, The use of fractal properties of echo signals for acoustic classification of bottom sediments, Acustica/Acta Acustica, Vol. 86, 276-282. 2000.
  • [10] H. M. Hastings, G. Sugihara: Fractals. A user’s guide for the natural sciences. Oxford University Press, 1994, Oxford, New York, Tokyo, 248.
  • [11] D. A. Rothrock, A. S. Thorndike: Geometric properties of the underside of sea ice. J. Geophys. Res., Vol. 85, 3955-3963, 1980.
  • [12] S. Mallat , A theory for multiresolution signal decomposition: the wavelet representation, IEEE Pattern Anal. and Machine Intell., Vol. 11 (7), 674-693, 1989.
  • [13] J.C. Bezdeck, R. Ehrlich, W.Full, , FCM: Fuzzy CMeans Algorithm, Computers and Geoscience, Vol. 10 (2-3), 191-203, 1984.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWM1-0007-0030
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