Self-organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process

  • Andrzej Stateczny
  • Marta Wlodarczyk-Sielicka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8537)


The article presents the reduction problems of hydrographic big data for the needs of gathering sound information for Navigation Electronic Chart (ENC) production. For the article purposes, data was used from an interferometric sonar, which is a modification of a multi-beam sonar. Data reduction is a procedure meant to reduce the size of the data set, in order to make them easier and more effective for the purposes of the analysis. The authors‘ aim is to examine whether artificial neural networks can be used for clustering data in the resultant algorithm. Proposed solution based on Kohonen network is tested and described. Experimental results of investigation of optimal network configuration are presented.


Kohonen network big data hydrography 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Lubczonek, J.: Application of GIS Techniques in VTS Radar Stations Planning. In: Kawalec, A., Kaniewski, P. (eds.) 2008 International Radar Symposium, Wroclaw, pp. 277–280 (2008)Google Scholar
  2. 2.
    Weintrit, A., Kopacz, P.: Computational Algorithms Implemented in Marine Navigation Electronic Systems. In: Mikulski, J. (ed.) TST 2012. CCIS, vol. 329, pp. 148–158. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Lubczonek, J., Stateczny, A.: Aspects of spatial planning of radar sensor network for inland waterways surveillance. In: 6th European Radar Conference (EURAD 2009). European Radar Conference-EuRAD, Rome, pp. 501–504 (2009)Google Scholar
  4. 4.
    Przyborski, M., Pyrchla, J.: Reliability of the navigational data. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) Intelligent Information Processing and Web Mining. ASC, vol. 22, pp. 541–545. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Stateczny, A.: Artificial neural networks for comparative navigation. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 1187–1192. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Przyborski, M.: Possible determinism and the real world data. Physica A-Statistical Mechanics and its Applications 309(3-4), 297–303 (2002)CrossRefGoogle Scholar
  7. 7.
    Stateczny, A.: Methods of comparative plotting of the ship’s position. In: Brebbia, C., Sciutto, G. (eds.) Maritime Engineering & Ports III, Rhodes. Water Studies Series, vol. 12, pp. 61–68 (2002)Google Scholar
  8. 8.
    Maleika, W., Palczynski, M., Frejlichowski, D.: Effect of Density of Measurement Points Collected from a Multibeam Echosounder on the Accuracy of a Digital Terrain Model. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012, Part III. LNCS (LNAI), vol. 7198, pp. 456–465. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Lubczonek, J., Stateczny, A.: Concept of neural model of the sea bottom surface. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing. ASC, vol. 19, pp. 861–866. Physica, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Lubczonek, J.: Hybrid neural model of the sea bottom surface. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 1154–1160. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Stateczny, A.: The neural method of sea bottom shape modelling for the spatial maritime information system. In: Brebbia, C., Olivella, J. (eds.) Maritime Engineering and Ports II, Barcelona. Water Studies Series, vol. 9, pp. 251–259 (2000)Google Scholar
  12. 12.
    Maleika, W.: The influence of track configuration and multibeam echosounder parameters on the accuracy of seabed DTMs obtained in shallow water. Earth Science Informatics 6(2), 47–69 (2013)CrossRefGoogle Scholar
  13. 13.
    Stateczny, A., Kazimierski, W.: Determining Manoeuvre Detection Threshold of GRNN Filter in the Process of Tracking in Marine Navigational Radars. In: Kawalec, A., Kaniewski, P. (eds.) 2008 Proceedings International Radar Symposium, Wroclaw, pp. 242–245 (2008)Google Scholar
  14. 14.
    Balicki, J., Kitowski, Z., Stateczny, A.: Extended Hopfield Model of Neural Networks for Combinatorial Multiobjective Optimization Problems. In: 2nd IEEE World Congress on Computational Intelligence, Anchorage, pp. 1646–1651 (1998)Google Scholar
  15. 15.
    Stateczny, A., Kazimierski, W.: A comparison of the target tracking in marine navigational radars by means of GRNN filter and numerical filter. In: 2008 IEEE Radar Conference, Rome, vol. 1-4, pp. 1994–1997 (2008)Google Scholar
  16. 16.
    Stateczny, A., Kazimierski, W.: Selection of GRNN network parameters for the needs of state vector estimation of manoeuvring target in ARPA devices. In: Romaniuk, R.S. (ed.) Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV, Wilga. Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE), vol. 6159, pp. F1591–F1591(2006)Google Scholar
  17. 17.
    Stateczny, A.: Neural manoeuvre detection of the tracked target in ARPA systems. In: Katebi, R. (ed.) Control Applications in Marine Systems 2001 (CAMS 2001), Glasgow. IFAC Proceedings Series, pp. 209–214 (2002)Google Scholar
  18. 18.
    Chung, K., Huang, Y., Wang, J., et al.: Speedup of color palette indexing in self-organization of Kohonen feature map. Expert Systems with Applications 39(3), 2427–2432 (2012)CrossRefGoogle Scholar
  19. 19.
    Ciampi, A., Lechevallier, Y.: Multi-level Data Sets: An Approach Based on Kohonen Self Organizing Maps. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 353–358. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  20. 20.
    de Almeida, C., de Souza, R., Candelas, A.: Fuzzy Kohonen clustering networks for interval data. Neurocomputing 99, 65–75 (2013)CrossRefGoogle Scholar
  21. 21.
    Du, Z., Yang, Y., Sun, Y., et al.: Map matching Using De-Noise Interpolation Kohonen Self-Organizing Maps. In: Conference: International Conference on Components, Packaging and Manufacturing Technology, Sanya. Key Engineering Materials, vol. 460-461, pp. 680–686 (2011)CrossRefGoogle Scholar
  22. 22.
    Guerrero, V., Anegon, F.: Reduction of the dimension of a document space using the fuzzified output of a Kohonen network. Journal of the American Society for Information Science and Technology 52(14), 1234–1241 (2001)CrossRefGoogle Scholar
  23. 23.
    Rasti, J., Monadjemi, A., Vafaei, A.: Color reduction using a multi-stage Kohonen Self-Organizing Map with redundant features. Expert Systems with Applications 38(10), 13188–13197 (2011)CrossRefGoogle Scholar
  24. 24.
    Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43(1), 59–69 (1982)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Kohonen, T.: The Self-Organizing Map. Proceedings of The IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  26. 26.
    Wlodarczyk-Sielicka, M.: 3D Double Buffering method in the process of hydrographic chart production with geodata taken from interferometry multibeam echo sounder. Annals of Geomantic, issue X 7(57), 101–108 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Andrzej Stateczny
    • 1
  • Marta Wlodarczyk-Sielicka
    • 2
  1. 1.Marine Technology Ltd.SzczecinPoland
  2. 2.Maritime UniversitySzczecinPoland

Personalised recommendations