2D COORDINATE TRANSFORMATION USING ARTIFICIAL NEURAL NETWORKS

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Konakoglu B., Cakir L., Gokalp E.

3rd International GeoAdvances Workshop / ISPRS Workshop on Multi-dimensional and Multi-Scale Spatial Data Modeling, İstanbul, Türkiye, 16 - 17 Ekim 2016, ss.183-186 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.5194/isprs-archives-xlii-2-w1-183-2016
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.183-186
  • Anahtar Kelimeler: Feed Forward Back Propagation, Cascade Feed Forward Back Propagation, Radial Basis Function Neural Network, 2D Coordinate Transformation, EARTH ORIENTATION PARAMETERS, PREDICTION
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

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.