Abstract
This paper proposes a novel solution to process sonar images. It uses intensity Hough transformation to find out line-type moving objects in B-mode images of sonar. Considering that objects in sonar B-mode images always have enough values of intensity and are shown as local peaks, mathematical morphology is adopted to restrain noises, and extract the peaks. The intensity images are involved, which are different from the binary images used by standard Hough transformation. Intensity accumulation is performed in accumulation space. Line-type moving objects are discovered when the accumulation exceeds the preset threshold. The approach is suitable for a variety of underwater environments due to the independence on the model of reverberation. The experimental result illustrates the effectiveness and robustness of the novel solution.
This work was supported by the Development Foundation of Shanghai Municipal Commission of Science and Technology (11dz1205902), and the Leading Academic Discipline Project of Shanghai Municipal Education Commission (J50104).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Greco, M., Stinco, P., Gini, F.: Identification and Analysis of Sea Radar Clutter Spikes. IET Radar, Sonar & Navigation 4, 239–250 (2010)
Huang, J.G., Cui, X.D., Wang, R.H.: Modeling and Simulation of Space-time Reverberation for Active Sonar Array. In: 2010 IEEE Region 10 Conference, TENCON 2010, pp. 2083–2086. IEEE Press, Fukuoka (2010)
Lingevitch, J.F., LePage, K.D.: Parabolic Equation Simulations of Reverberation Statistics From Non-Gaussian-Distributed Bottom Roughness. IEEE Journal of Oceanic Engineering 35, 199–208 (2010)
Thorsos, E.I., Perkins, J.S.: Overview of the Reverberation Modeling Workshops. In: Proceedings of the International Symposium on Underwater Reverberation and Clutter, La Spezia, pp. 3–22 (2008)
Firoiu, I., Nafornita, C., Isar, D., Isar, A.: Bayesian Hyperanalytic Denoising of Sonar Images. Geoscience and Remote Sensing Letters 8, 1065–1069 (2011)
Liang, G.L., Liu, K., Lin, W.S.: A New Beam-forming Algorithm Based on Flank Acoustic Vector-sensor Array Sonar. In: 2009 International Conference on Wireless Communications & Signal Processing, pp. 1–5. IEEE Press, Nanjing (2009)
Leier, S., Zoubir, A.M.: Quality Assessment of Synthetic Aperture Sonar Images Based on a Single Ping Reference. In: OCEANS 2011 IEEE-Spain, pp. 1–4. IEEE Press, Santander (2011)
Yang, H.W., Li, X.L., Wu, J.P., Guo, H.T.: Complex Method for Speckle Noise Reduction in the Sonar Image from a Small Underwater Target. In: 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce, pp. 254–256. IEEE Press, Deng Leng (2011)
Tu, C., Du, S., van Wyk, B.J., Djouani, K., Hamam, Y.: High Resolution Hough Transform based on Butterfly Self-Similarity. Electronics Letters 47, 1360–1361 (2011)
Guerreiro, R.F.C., Aguiar, P.M.Q.: Incremental Local Hough Transform for Line Segment Extraction. In: 2011 18th IEEE International Conference on Image Processing, pp. 2841–2844. IEEE Press, Brussels (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, Q., Li, Y., Jiang, Y. (2012). Line-Type Moving Object Detection for Sonar Images. In: Zhang, W., Yang, X., Xu, Z., An, P., Liu, Q., Lu, Y. (eds) Advances on Digital Television and Wireless Multimedia Communications. Communications in Computer and Information Science, vol 331. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34595-1_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-34595-1_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34594-4
Online ISBN: 978-3-642-34595-1
eBook Packages: Computer ScienceComputer Science (R0)