Imagery acquired by airborne sensors is used to address many different tasks in various fields of application. Many of those tasks require the imagery to be georeferenced, i.e. providing a relation between the image coordinate of an image pixel and the real world coordinate of the location on the earth's surface it represents. The georeference of airborne imagery is usually implemented via GPS and INS sensors on board the sensor platform, but potential problems, such as transmission problems, jamming or temporal sensor malfunction, together with a potentially poor knowledge of ground elevation, can render location information accuracy less than sufficient for a given task. We established an image registration workflow which has the capability to improve the georeference of an image in such cases by matching it with a reference image with a satisfying georeference accuracy, i.e. an image covering the same area at a similar resolution. This is achieved by four steps in which an object extraction step is followed by a contour extraction step, which is then followed by a contour point reduction step and finally by a contour matching step. This approach has proven to be both feasible and robust to appearance unsimilarity between the image and the reference image. As each step of the workflow has well defined interfaces for both their input and output, we can easily exchange the methods implementing the operation to be performed in the respective step. This allows us to easily and efficiently evaluate different methods for these operations. The scope of this work was both the implementation of a new method for the Transformation Estimation step, namely the Downhill Simplex Method in Multidimensions, and the systematic analysis of the quantitive influence of different methods and their parametrizations for the Contour Point Number Reduction and the Transformation Estimation steps on the accuracy of the georeference.