Object extraction in the context of an image registration workflow

With the advent of affordable drone systems, imagery from airborne sensors has become available for addressing many different tasks in various fields of application. For some of these tasks the imagery has to come with a georeference satisfying certain accuracy requirements. If we want to perform such a task and the accuracy of GPS and INS sensors onboard the sensor platform cannot match accuracy requirements or location information is faulty or unavailable, we need to establish a georeference or improve the inaccurate existing one. We do this with our image registration workflow. It matches the contours of objects present both in the imagery and a reference image which comes with a georeference satisfying the accuracy requirement of the task to be performed. This approach has proven to be both feasible and robust to appearance unsimilarity between the image and the reference image, enabling to use a reference that is rather unsimilar in appearance to the image. The workflow comprises four steps, namely extracting the objects, extracting their contours, reducing the amount of contour points and finally matching them. To improve the performance of our workflow, we aspire to improve the performance of each of the four steps individually. While previous work has focused on the finetuning of the three latter steps keeping the object extracting method and thus step one fixed for the time being, the scope of this work was the implementation of a novel object extraction method and its evaluation in the context of the workflow. Long line shaped objects such as road networks are likely to be present both in the image and the reference despite their possible unsimilarity in appearance. The method extracts such objects after growing them by merging smaller individual line-shaped objects if certain merge criteria is met