Image Georeferencing using Artificial Neural Network Compared with Classical Methods


  • Zahraa Ezzulddin Hussein Surveying Engineering, College of Engineering, University of Baghdad, Baghdad, Iraq



Coordinates, Neural Network, Affine, georeferencing, transformation


Georeferencing process is one of the most important prerequisites for various geomatics applications; for example, photogrammetry, laser scan analysis, remotely sensing, spatial and descriptive data collection, and others. Georeferencing mostly involves the transformation of coordinates obtained from images that are inhomogeneous due to accuracy differences. The georeferencing depends on image resolution and accuracy level of measurements of reference points ground coordinates.  Accordingly, this study discusses the subject of coordinate’s transformation from the image to the global coordinates system (WGS84) to find a suitable method that provides more accurate results. In this study, the Artificial Neural Network (ANN) method was applied, in addition to several numerical methods, namely the Affine divided difference, Newton’s divided difference, and polynomial transformation. The four methods were modelled and coded using Matlab programming language based on an image captured from Google Earth. The image was used to determine reference points within the study area (University of Baghdad campus).  The findings of this study showed that the ANN enhanced the results by about 50% in terms of accuracy and 90% in terms of homogeneity, compared with the other methods.


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How to Cite

Hussein, Z. E. . (2021). Image Georeferencing using Artificial Neural Network Compared with Classical Methods. Iraqi Journal of Science, 62(12), 5024–5034.



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