Abstract Image template matching is a main task in photogrammetry and computer vision. The matching can be used to automatically determine the 3D coordinates of a point. A firstborn image matching method in fields of photogrammetry and computer vision is area-based matching, which is based on correlation measuring that uses normalised cross-correlation. However, this method fails at a discontinuous edge and at the area of low illumination or at geometric distortion because of changes in imaging location. Thus, these points are considered outliers. The proposed method measures correlations, which is based on normalised cross-correlation, at each point by using various sizes of window and then considering the probability of correlations for each window. Thereafter, the determined probability values are integrated. On the basis of a specific threshold value, the point of maximum total probability correlation is recognised as a corresponding point. The algorithm is applied to aerial images for Digital Surface Model (DSM) generation. Results show that the corresponding points are identified successfully at different locations, especially at a discontinuous point, and that a Digital Surface Model of high resolution is generated.

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[49] Zitova B. and Flusser J., “Image Registration Methods: A Survey,” Image and Vision Computing, vol. 21, no. 11, pp. 977-1000, 2003. Haval Sadeq is a lecturer and head of the Geomatics (Surveying) Eng. dep. at Salahaddin University-Erbil, Iraq. He received his BSC degree fromSalahaddin University-Erbil, in 1996. He received his MSc degree in photogrammetry fromSalahaddin University-Erbil, Iraq, in 2000. In 2015 he received his PhD degree in Geomaticsfrom GlasgowUniversity, United Kingdom. His research interests are in photogrammetry, image matching, and, 3D city modelling from imagery.