Synthesis and wavelet analysis of side-scan sonar sea bottom imagery

• Autorzy: Tęgowski J. , Zieliński A.
• Języki publikacji: EN

Abstrakty

EN

A backscattered side-scan sonar signal contains indirect information about the scattering surface, namely, the bottom sediment, character of bottom surface, and seafloor relief. This paper presents a method of automatic estimation of height of seafloor characteristic objects and micro relief reconstruction applying the measured shadow length. The proposed method utilises coefficients of two-dimensional discrete wavelet decomposition of a side-scan sonar image as the input to the self-organised neural network classification algorithm. The heights of seafloor characteristic objects were the basis for synthesis of a three-dimensional map of the bottom surface. The computations were conducted for data recorded in Hornsund Fjord (Spitsbergen Island, Svalbard Archipelago) during a habitat mapping experiment and for synthetic data. The verification of the proposed algorithm was made by comparison of computed results with calibrated video recordings.

• Wydawca: Polskie Towarzystwo Akustyczne. Oddział Gdański
• Czasopismo: Hydroacoustics
• Rocznik: 2006
• Tom: Vol. 9
• Strony: 199--208
• Opis fizyczny: Bibliogr. 11 poz., rys.

Twórcy

autor

Tęgowski J.

autor

Zieliński A.

• Institute of Oceanology, Polish Academy of Sciences, Powstancow Warszawy 55, 81-712 Sopot, Poland,

Bibliografia

• [1] A. Zielinski, J. Tegowski, Acoustic Echo Formation – A Filter Theory Approach, Proceedings of Oceans 2003 Marine Technology and Ocean Science Conference, 22-26 September 2003, pp. 1234-1238, 2003.
• [2] Ph. Blondel, B.J. Murton, Handbook of Seafloor Sonar Imagery, PRAXIS-Wiley, Chichester, pp. 314, 1997.
• [3] R. Zhang, P. Tsai, J.E. Cryer and M. Shah, Shape form shading: a survey, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 21, No. 8, pp. 690-705,1999.
• [4] E. Dura, J. Bell and D. Lane, Reconstruction of textured seafloors from side-scan sonar images, IEEE Proc.-Radar Sonar Navig., Vol. 151, No. 2, pp. 114-126, 2004.
• [5] S. Reed, Y. Petillot and J. Bell, Automated approach to classification of mine-like objects in sidescan sonar using highlight and shadow information, IEEE Proc.-Radar Sonar Navig., Vol. 151, No. 1, pp. 48-56, 2004.
• [6] E. Dura, J. Bell and D. Lane, Superellipse fitting for the classification of mine-like shapes in side-scan sonar images, Proc. MTS/IEEE Oceans, Conf. and Exhibition, pp. 23-28, 2002.
• [7] S. Reed, J. Bell, and Y. Petillot, Unsupervised segmentation of object shadow and highlight using statistical snakes, Proc. Autonomous Underwater Vehicle and Ocean Modelling Networks: GOATS 2000, SACLANTEN Conf. Proc. CP-46, La Spezia, Italy, pp. 221-236, 2001.
• [8] S. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Pattern Anal. and Machine Intell., Vol. 11, No. 7, pp. 674-693, 1989.
• [9] A. Cohen, I. Daubechies and J.C. Feauveau, Biorthogonal basis of compactly supported wavelets, Comm. Pure Appli. Math. , Vol. 45, pp. 485-560, 1992.
• [10] J.C. Bezdeck, R. Ehrlich and W. Full, FCM: Fuzzy CMeans Algorithm, Computers and Geoscience, Vol. 10, No. 2, pp. 191-203, 1984.
• [11] T. Kohonen, Adaptive, associative, and self-organizing functions in neural computing, Appl. Opt., Vol. 26, No. 23, pp. 4910-4918, 1987.