During the image matching process conjugate features need to be found automatically between the overlapping images. The surface model can be processed afterwards by calculation of height differences based on the measurement of the disparity between corresponding pixels. The applied algorithm works according a coarse-to-fine hierarchical matching strategy. Image pyramids consist of different versions of an image at exponentially decreasing resolutions. The bottom level of the pyramid contains the original image. The matching results of each higher pyramid level are used as approximations in the successive, lower level. At each level also an intermediate DSM is generated from the matched features and is refined through the image pyramid. Based on all data in each pyramid level, the matching parameters are fine-tuned progressively.

The matching algorithm is a combination of feature point, grid point and 3D edge matching. This redundancy leads to better constraints and more reliable results. Grid point matching is especially valuable in areas with less texture where conjugate feature points are hard to detect. For each grid point to be matched in the first image, the matching algorithm searches for the conjugate pixel in the other images that correlates the most by shifting a kernel of certain size along the epipolar line. A correlation constraint is used to identify possible matching candidates. The geometrically constrained cross-correlation or GC 3 method is an extension of the standard cross-correlation technique (Zhang & Gruen, 2006). In case of more than one matching candidate, the information of multiple images, i.e. more then two, can provide geometric constraints which assist to identify a unique matching solution.