Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W1, 183-186, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
26 Oct 2016
B. Konakoglu, L. Cakır, and E. Gökalp KTU, Engineering Faculty, 61080 Trabzon, Turkey
Keywords: Feed Forward Back Propagation, Cascade Feed Forward Back Propagation, Radial Basis Function Neural Network, 2D Coordinate Transformation Abstract. Two coordinate systems used in Turkey, namely the ED50 (European Datum 1950) and ITRF96 (International Terrestrial Reference Frame 1996) coordinate systems. In most cases, it is necessary to conduct transformation from one coordinate system to another. The artificial neural network (ANN) is a new method for coordinate transformation. One of the biggest advantages of the ANN is that it can determine the relationship between two coordinate systems without a mathematical model. The aim of this study was to investigate the performances of three different ANN models (Feed Forward Back Propagation (FFBP), Cascade Forward Back Propagation (CFBP) and Radial Basis Function Neural Network (RBFNN)) with regard to 2D coordinate transformation. To do this, three data sets were used for the same study area, the city of Trabzon. The coordinates of data sets were measured in the ED50 and ITRF96 coordinate systems by using RTK-GPS technique. Performance of each transformation method was investigated by using the coordinate differences between the known and estimated coordinates. The results showed that the ANN algorithms can be used for 2D coordinate transformation in cases where optimum model parameters are selected.
Conference paper (PDF, 1030 KB)

Citation: Konakoglu, B., Cakır, L., and Gökalp, E.: 2D COORDINATE TRANSFORMATION USING ARTIFICIAL NEURAL NETWORKS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W1, 183-186,, 2016.

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