Metoda profili w budowie numerycznego modelu rzeźby terenu

• Autorzy: Stateczny A. , Kozak M.

Warianty tytułu

EN

Building digital terrain model using cross-sections method

• Języki publikacji: PL

Abstrakty

EN

A numerical model of topographic relief can be defined as a set of measurement points with an interpolation algorithm. It enables the visualisation of 3D spatial data; therefore, the scope of its application is very wide. On the one hand it is applied for simulating the development of phenomena, and on the other, for designing the location of facilities and buildings. With the growing number of DTC (Digital Topographic Chart) users, there also increase their requirements concerning data quality (accuracy, reliability, up-to-dateness) and the possibilities of analysis in real time. The article presents research on the DTC construction method, which stresses the organisation of data recording, limiting them to minimum, at the same time aiming at the possibility of analysis in real time, and the construction of the model with assumed accuracy. Modern measurement systems permit the automatic acquisition of very large data sets. Traditional construction methods of a numerical topographical relief model most frequently process the data sets to the shape of a regular GRID net. At nodal points, the height/depth of the terrain is calculated on the basis of an interpolation algorithm. A problem encountered while applying a GRID net for the construction of a DTC is its large size. Applying a GRID net for 1 sq. km with net size of 1m we will obtain 1 million points. For larger areas the number of points will be proportionately higher. Using such a number of data in real time is impossible; their very storage is a problem in itself. By additionally applying methods based on GRID net for areas with irregular shapes (e.g. a fairway) a rectangleshaped net is obtained as a result, with a large number of unnecessary and distorted data. The method of profiles used for the construction of a numerical model of topographical relief is dedicated for fairway-type areas. The purpose of this method is to restrict the number of data necessary for reproducing a 3D bottom model. The time of constructing the method is inessential, as the model will not be constructed on a vessel unit, and it will be changed only if new measurement data are acquired. The accuracy of the reconstructed surface is a very important criterion, assumed by the user in advance as the maximum error. The input data for the method are measurement points with the designated fairway axis. The data are divided into sections, and in the next stage the system of coordinates is turned around and shifted for each section, so that the width of the considered data fragment should be as small as possible. Next, cross-sections are built for all sections in equal distances. Based on interpolations between sections (maximum error), it is checked if the distances between sections are correct. If the value of maximum error is too large, a successive profile is added. If the value of maximum error is too small, it is checked if a profile is not an excess profile. The method of profiles restricts the number of data necessary for a 3D spatial visualisation; it does not contain excessive and distorted data, which occur in the case of GRID-net based methods. Adaptively selected cross-sections will be kept on the unit, and they will be visualised in real time. The method of profiles makes it possible to construct a numerical model of topographical relief with previously assumed accuracy.

Słowa kluczowe

PL

numeryczny model rzeźby terenu; metoda profili; sieci RBF

EN

digital terrain model (DTM); cross-sections metod; RBF networks

• Wydawca: Polskie Towarzystwo Informacji Przestrzennej
• Czasopismo: Roczniki Geomatyki
• Rocznik: 2005
• Tom: T. 3, z. 1
• Strony: 147--153
• Opis fizyczny: Bibliogr. 13 poz.

Twórcy

autor

Stateczny A.

• Akademia Morska w Szczecinie

autor

Kozak M.

• Politechnika Szczecińska

Bibliografia

• 1. Balicki J., Kitowski Z., Stateczny A., 1999: Artificial Neural Networks for Modelling of Spatial Shape of Sea Bottom, IV Conference of Neural Networks and Their Applications, Zakopane.
• 2. Chen S., Wu Y., Luk B.L., 1999: Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks, IEEE Transactions on Neural Networks 10(5), 1239–1243
• 3. Chen S., Cowan C.F.N., Grant P.M., 1991: Orthogonal least squares learning algorithm for radial basis function networks, IEEE Transactions on Neural Networks 2(2), 302-309.
• 4. Chen S., Chng E.S., Alkadhimi K., 1996: Regularized orthogonal least squares algorithm for constructing radial basis function networks, International Journal of Control 64(5), 829-837.
• 5. Kurczyński Z., 1998: Numeryczny model terenu – standardy. Opracowanie dla zespołu powołanego przez Głównego Geodetę Kraju.
• 6. Łubczonek J., Stateczny A., 2002: Concept of neural model of the sea bottom surface, 6th International Conference Neural Networks and Soft Computing, Zakopane.
• 7. Sandwell D.T., 1987: Biharmonic Spline interpolation of GEOS-3 and sea sat altimeter data, Geophysical Research Letters 14(2), 139-142.
• 8. Stateczny A., Kamiński W., 2003: The mathematical model of 3D fairway obtained by cross-sections and orthogonal networks RBF used for steering vessels, 9th IEEE International Conference on Methods and Models in Automation and Robotics, Międzyzdroje.
• 9. Stateczny A., Praczyk T., 2000: Neuronowa metoda modelowania kształtu dna morskiego, X Konferencja Naukowo-Techniczna Systemy Informacji Przestrzennej, Warszawa.
• 10. Stateczny A., 2000: The neural method of sea bottom shape modelling for spatial maritime information system, WIT Press Southampton, Boston.
• 11. Stateczny A. (red.), 2004: Metody nawigacji porównawczej. Gdańskie Towarzystwo Naukowe. Gdańsk.
• 12. Stateczny A., 2001: Nawigacja porównawcza. Gdańskie Towarzystwo Naukowe. Gdańsk.
• 13. Strumiłło P., Kamiński W., 2001: Orthogonalisation Procedure for Training Radial Basis Functions Neural Networks, Technical Sciences, Polish Academy of Science, Warszawa.