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Clustering Bathymetric Data for Electronic Navigational Charts

Published online by Cambridge University Press:  09 February 2016

Marta Wlodarczyk–Sielicka  and
Andrzej Stateczny
Marta Wlodarczyk–Sielicka*
Affiliation:
(Institute of Geoinformatics, Faculty of Navigation, Maritime University of Szczecin, Poland)
Andrzej Stateczny
Affiliation:
(Marine Technology Ltd, Szczecin, Poland)
*
(E-mail: [email protected])
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Abstract

An electronic navigational chart is a major source of information for the navigator. The component that contributes most significantly to the safety of navigation on water is the information on the depth of an area. For the purposes of this article, the authors use data obtained by the interferometric sonar GeoSwath Plus. The data were collected in the area of the Port of Szczecin. The samples constitute large sets of data. Data reduction is a procedure to reduce the size of a data set to make it easier and more effective to analyse. The main objective of the authors is the compilation of a new reduction algorithm for bathymetric data. The clustering of data is the first part of the search algorithm. The next step consists of generalisation of bathymetric data. This article presents a comparison and analysis of results of clustering bathymetric data using the following selected methods: K-means clustering algorithm, traditional hierarchical clustering algorithms and self-organising map (using artificial neural networks).

Keywords

Electronic Chart Display and Information SystemBathymetryData reduction
Type
Research Article
Information
The Journal of Navigation , Volume 69 , Issue 5 , September 2016 , pp. 1143 - 1153
Copyright
Copyright © The Royal Institute of Navigation 2016 

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References

REFERENCES

Balicki, J., Kitowski, Z. and Stateczny, A. (1998). Extended Hopfield Model of Neural Networks for Combinatorial Multiobjective Optimization Problems. In Proceedings of the 2nd IEEE World Congress on Computational Intelligence, Anchorage, USA, pp. 1646–1651.Google Scholar
Ciampi, A., Lechevallier, Y. and Clustering, L. (2000). Multi-level Data Sets: an Approach Based on Kohonen Self Organizing Maps. Lecture Notes in Computer Science, 1910, 353–358.CrossRefGoogle Scholar
Hyla, T., Wawrzyniak, N. and Kazimierski, W. (2015). Model of Collaborative Data Exchange for Inland Mobile Navigation. In Proceedings of Soft Computing in Computer and Information Science Conference, Miedzyzdroje, Poland. Advances in Intelligent Systems and Computing, 342, 435–444.CrossRefGoogle Scholar
Janowski, A., Nowak, A., Przyborski, M. and Szulwic, J. (2014). Mobile Indicators in GIS and GPS Positioning Accuracy in Cities. In Proceedings of the Joint Rough Set Symposium, Granada and Madrid, Spain, Kryszkiewicz et al. (Eds), Lecture Notes in Artificial Intelligence, 8537, pp. 309–318.CrossRefGoogle Scholar
Kazimierski, W. and Stateczny, A. (2015). Radar and Automatic Identification System Track Fusion in an Electronic Chart Display and Information System. The Journal of Navigation 68(6), 1141–1154.CrossRefGoogle Scholar
Kohonen, T. (1982). Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics, 43(1), 59–69.CrossRefGoogle Scholar
Li, Z. (2007). Algorithmic Foundation of Multi-scale Spatial Representation. CRC Press.Google Scholar
Liu, T., Zhao, D. and Pan, M. (2014). Generating 3D Depiction for a Future ECDIS Based on Digital Earth. The Journal of Navigation, 67(6), 1049–1068.CrossRefGoogle Scholar
Lubczonek, J. (2004). Hybrid Neural Model of the Sea Bottom Surface, In Artificial Intelligence and Soft Computing – ICAISC 2004, Zakopane, Poland, Rutkowski, L., Siekmann, J., Tadeusiewicz, R. et al. (Eds), Lecture Notes in Artificial Intelligence, 3070, 1154–1160.Google Scholar
Lubczonek, J. and Stateczny, A. (2003). Concept of Neural Model of the Sea Bottom Surface. In Proceedings of the Sixth International Conference on Neural Networks and Soft Computing, Zakopane, Poland, Rutkowski, L. and Kacprzyk, J. (Eds), Book Series: Advances in Soft Computing, 19, pp. 861–866.CrossRefGoogle Scholar
Maleika, W. (2015a). Moving Average Optimization in Digital Terrain Model Generation Based on Test Multibeam Echosounder Data. Geo-Marine Letters, 35, 61–68.CrossRefGoogle Scholar
Maleika, W. (2015b). The Influence of the Grid Resolution on the Accuracy of the Digital Terrain Model Used in Seabed Modelling. Marine Geophysical Research, 36, 35–44.CrossRefGoogle Scholar
Mignoti, S. and Lima, J. (2006). Comparing SOM Neural Network with Fuzzy c-means, K-Means and Traditional Hierarchical Clustering Algorithms. European Journal of Operational Research, 174, 1742–1759.Google Scholar
Przyborski, M. (2002). Possible determinism and the real world data. Physica A – Statistical Mechanics and its Applications, 309(3–4), 297–303.CrossRefGoogle Scholar
Stateczny, A. (2000). The Neural Method of Sea Bottom Shape Modelling for the Spatial Maritime Information System. Maritime Engineering and Ports II. Barcelona, Spain, Brebbia, C. A., and Olivella, J. (Eds), Book Series: Water Studies Series, 9, pp. 251–259.Google Scholar
Stateczny, A. (2002a). Methods of Comparative Plotting of the Ship's Position, Maritime Engineering & Ports III. Rhodes, Greece, Brebbia, C. A. and Sciutto, G. (Eds), Book Series: Water Studies Series, 12, pp. 61–68.Google Scholar
Stateczny, A. (2002b). Neural Manoeuvre Detection of the Tracked Target in ARPA Systems, In Control Applications in Marine Systems 2001 (CAMS 2001), Glasgow, UK, Katebi, R. (Ed.), IFAC Proceedings Series, pp. 209–214.CrossRefGoogle Scholar
Stateczny, A. (2004). Artificial Neural Networks for Comparative Navigation, In Artificial Intelligence and Soft Computing – ICAISC 2004, Zakopane, Poland, Rutkowski, L., Siekmann, J. Tadeusiewicz, R. et al. (Eds), Lecture Notes in Artificial Intelligence, 3070, 1187–1192.Google Scholar
Stateczny, A. and Bodus-Olkowska, I. (2014). Hierarchical Hydrographic Data Fusion for Precise Port Electronic Navigational Chart Production. In Telematics in the Transport Environment, Proceedings of the 14th International Conference on Transport Systems Telematics, TST 2014, Katowice-Ustroń, Poland, Mikulski, J. (Ed.), Communications in Computer and Information Science, 471, 359–368.Google Scholar
Stateczny, A. and Bodus-Olkowska, I. (2015). Sensor Data Fusion Techniques for Environment Modelling. In Proceedings of the 16th International Radar Symposium (IRS), Dresden, Germany, Rohling, H. (Ed.), International Radar Symposium Proceedings, pp. 1123–1128.Google Scholar
Stateczny, A. and Kazimierski, W. (2013). Sensor Data Fusion in Inland Navigation. In Proceedings of the International Radar Symposium (IRS), Dresden, Germany, Rohling, H. (Ed.), pp. 264–269.Google Scholar
Stateczny, A. and Wlodarczyk-Sielicka, M. (2014). Self-Organizing Artificial Neural Networks into Hydrographic Big Data Reduction Process. In Proceedings of the Joint Rough Set Symposium, Granada and Madrid, Spain, Kryszkiewicz, M. et al. (Eds), Lecture Notes in Artificial Intelligence, 8537, pp. 335–342.Google Scholar
Tsou, M.-C. (2010). Integration of a Geographic Information System and Evolutionary Computation for Automatic Routing in Coastal Navigation. The Journal of Navigation, 63(2), 323–341.CrossRefGoogle Scholar
Wawrzyniak, N. and Hyla, T. (2014). Managing Depth Information Uncertainty in Inland Mobile Navigation Systems. In Proceedings of the Joint Rough Set Symposium, Granada and Madrid, Spain,Kryszkiewicz et al. (Eds), Lecture Notes in Artificial Intelligence, 8537, pp. 343–350.Google Scholar
Wlodarczyk-Sielicka, M. and Stateczny, A. (2015). Selection of SOM Parameters for the Needs of Clusterisation of Data Obtained by Interferometric Methods. In Proceedings of the 16th International Radar Symposium (IRS), Dresden, Germany, Rohling, H. (Ed.) International Radar Symposium Proceedings, pp. 1129–1134.Google Scholar
Ye, H., Meng, X., Yang, L. and Anand, S. (2014). Development of a Digital Accident Hotspot Map for ADAS Applications Using Geospatial Methods in GIS. The Journal of Navigation, 67(3), pp. 353–369.CrossRefGoogle Scholar