Neural networks for the generation of sea bed models using airborne lidar bathymetry data

• Autorzy: Kogut T. , Niemeyer J. , Bujakiewicz A.

Identyfikatory

DOI

10.1515/geocart-2016-0007

• Języki publikacji: EN

Abstrakty

EN

Various sectors of the economy such as transport and renewable energy have shown great interest in sea bed models. The required measurements are usually carried out by ship-based echo sounding, but this method is quite expensive. A relatively new alternative is data obtained by airborne lidar bathymetry. This study investigates the accuracy of these data, which was obtained in the context of the project ‘Investigation on the use of airborne laser bathymetry in hydrographic surveying’. A comparison to multi-beam echo sounding data shows only small differences in the depths values of the data sets. The IHO requirements of the total horizontal and vertical uncertainty for laser data are met. The second goal of this paper is to compare three spatial interpolation methods, namely Inverse Distance Weighting (IDW), Delaunay Triangulation (TIN), and supervised Artificial Neural Networks (ANN), for the generation of sea bed models. The focus of our investigation is on the amount of required sampling points. This is analyzed by manually reducing the data sets. We found that the three techniques have a similar performance almost independently of the amount of sampling data in our test area. However, ANN are more stable when using a very small subset of points.

Słowa kluczowe

EN

airborne lidar bathymetry; interpolation; neural networks; inverse distance weighting; Delaunay triangulation

PL

interpolacja; sieci neuronowe; triangulacja Delaunaya

• Wydawca: Komitet Geodezji PAN
• Czasopismo: Geodesy and Cartography
• Rocznik: 2016
• Tom: Vol. 65, no. 1
• Strony: 41--53
• Opis fizyczny: Bibliogr. 23 poz., rys., tab., wykr.

Twórcy

autor

Kogut T.

• Koszalin University of Technology, Department of Geoinformatics, 2 Śniadeckich St., 75-453 Koszalin, Poland

autor

Niemeyer J.

• Leibniz Universität Hannover, Institute of Photogrammetry and GeoInformation, D-30167 Hannover, 1 Nienburger St., Germany

autor

Bujakiewicz A.

• Koszalin University of Technology, Department of Geoinformatics, 2 Śniadeckich St., 75-453 Koszalin, Poland

Bibliografia

• [1] Airborne Hydrography AB, (2013). Chiroptera – Technical Specification. http://www.airbornehydrography.com/chiroptera (29.4.2013).
• [2] Azpurua, M. and Ramos, K. (2010). A comparison of spatial interpolation methods for estimation of average electromagnetic field magnitude. Progress In Electromagnetics Research M, Vol. 14: 135-145.
• [3] Bagheri, H., Sadeghian, S. and Sadjadi, S. Y. (2014). The Assessment of using an Intelligent Algorithm for the Interpolation of Elevation in the DTM Generation. PFG – Photogrammetrie, Fernerkundung, Geoinformation, Vol. 3 (2014): 197–208.
• [4] Bai, Y., Zhang, H. and Hao, Y. (2009). The performance of the backpropagation algorithm with varying slope of the activation function. Chaos, Solitons & Fractals, Vol. 40(1): 69–77.
• [5] Bell, J. (2014). Machine Learning: Hands-On for Developers and Technical Professionals, Wiley, 95.
• [6] Bishop, C. (2007). Pattern Recognition and Machine Learning. Springer.
• [7] Brouwer, P.A.I. (2008). Seafloor classification using a single beam echosounder, TU Delft.
• [8] Caudill, M. (1987). Neural networks primer, part I, AI Expert, Vol. 2(12) pp. 46 – 52.
• [9] Costa, B. M., Battista, T. A. and Pittman, S. J. (2009). Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reefecosystems, Remote Sensing of Environment, 113(5): 1082-1100.
• [10] de Berg, M., Cheong, O., van Kreveld, M. and Overmars, M. (2008). Computational Geometry: Algorithms and Applications, Springer, 191-218.
• [11] Duda, R O, Hart, P. and Stork, D. (2000). Pattern Classification. Second Edition, Wiley-Interscience.
• [12] Gumus, K. and Sen, A. (2013). Comparison of spatial interpolation methods and multi-layer neural networks different point distributions on a digital elevation model, Geodetski Vestnik, Vol. 57(3): 523.
• [13] Hagan, M.T. and Menhaj, M.B. (1994). Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Network, Vol. 5, pp. 989–993.
• [14] Hu, P., Liu, X. and Hu, H. (2009). Accuracy Assessment of Digital Elevation Models based on Approximation Theory, Photogrammetric Engineering and Remote Sensing, Vol. 75, No.1: 49-56.
• [15] International Hydrographic Organization, (2008). IHO Standards for Hydrographic Surveys, Special Publication N 44, 5th Edition.
• [16] Jain, A.K., Mao, J. and Mohiuddin, K. (1996). Artificial Neural Networks: A Tutorial. Computer, Vol. 3, IEEE, 31-44.
• [17] Karsoliya, S. (2012). Approximating Number of Hidden layer neurons in Multiple Hidden Layer BPNN Architecture, International Journal of Engineering Trends and Technology.
• [18] Levenberg, K. (1944). A method for the solution of certain non-linear problems in least squares, Quarterly Journal of Applied Mathematics, Vol. II(2): 164–168.
• [19] Marquardt, D. W. (1963). An algorithm for least-squares estimation of non-linear parameters, Journal of the Society for Industrial and Applied Mathematics, Vol. 11(2): 431–441.
• [20] Niemeyer, J., Kogut, T. and Heipke, C. (2014). Airborne Laser Bathymetry for Monitoring the German Baltic Sea Coast, Tagungsband der DGPF-Jahrestagung (Proceedings of the annual conference of the German Society for Photogrammetry, Remote Sensing and Geoinformation), Vol. 23.
• [21] Stefko, K. (2008). Application of MLBP Neural Network for Exercise ECG Test Records Analysis in Coronary Artery Diagnosis, Information Technologies in Biomedicine, Advances in Soft Computing, Vol. 47, Springer, pp 179-183.
• [22] Steinbacher, F., Pfennigbauer M., Aufleger, M. and Ullrich, A. (2012). High resolution airborne shallow water mapping, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Proceedings of the XXII ISPRS Congress, Vol. XXXIX-B1, Melbourne, Australia, pp. 55–60.
• [23] Zhong, D., Liu J., Li M. and Hao, C. (2008). NURBS reconstruction of digital terrain for hydropower engineering based on TIN model, Progress in Natural Science, Vol. 18, Issue 11: 1409–1415.