Multiple SAR interferograms with judiciously selected height sensitivities can be iteratively combined to create a high accuracy digital elevation map. An initial height estimate is refined by iteratively using larger baselines to obtain a height estimation accuracy limited by the spatial decorrelation of the antenna baseline. Spatial filtering is used to reduce the propagation of errors for accurate height estimation. Images containing regions isolated by phase discontinuities, as often found in urban environments, can be resolved by this iterative multi-baseline technique. Computationally demanding and potentially unreliable phase unwrapping is not required to determine scene elevation using SAR inMultiple SAR interferograms with judiciously selected height sensitivities can be iteratively combined to create a high accuracy digital elevation map. An initial height estimate is refined by iteratively using larger baselines to obtain a height estimation accuracy limited by the spatial decorrelation of the antenna baseline. Spatial filtering is used to reduce the propagation of errors for accurate height estimation. Images containing regions isolated by phase discontinuities, as often found in urban environments, can be resolved by this iterative multi-baseline technique. Computationally demanding and potentially unreliable phase unwrapping is not required to determine scene elevation using SAR interferometry.

Modern day cars are often equipped with a variety of sensors that collect information about the car and its surroundings. The stereo camera is an example of a sensor that in addition to regular images also provides distances to points in its environment. This information can, for example, be used for detecting approaching obstacles and warn the driver if a collision is imminent or even automatically brake the vehicle. Objects that constitute a potential danger are usually located on the road in front of the vehicle which makes the road surface a suitable reference level from which to measure the object's heights. This Master's thesis describes how an estimate of the road surface can be found to in order to make these height measurements. The thesis describes how the large amount of data generated by the stereo camera can be scaled down to a more effective representation in the form of an elevation map. The report discusses a method for relating data from different instances in time using information from the vehicle's motion sensors and shows how this method can be used for temporal filtering of the elevation map. For estimating the road surface two different methods are compared, one that uses a RANSAC-approach to iterate for a good surface model fit and one that uses conditional random fields for modeling the probability of different parts of the elevation map to be part of the road. A way to detect curb lines and how to use them to improve the road surface estimate is shown. Both methods for road classification show good results with a few differences that are discussed towards the end of the report. An example of how the road surface estimate can be used to detect obstacles is also included.

This work refers existing solutions and options for the task registration of photos to 3D model based on the previous knowledge of the geographic position of the camera. The contribution of the work are new ways and possibilities of the solution with the usage of graph algorithms. In this area, the work interests are useful points of interest detection in input data, a construction of graphs and graph matching possibilities.