In this paper, a simulation concept is presented for creating synthetic aperture radar (SAR) reflectivity maps based on ray tracing. Three-dimensional models of man-made objects are illuminated by a virtual SAR sensor whose signal is approximated by rays sent through the model space. To this end, open-source software tools are adapted and extended to derive output data in SAR geometry followed by creating the reflectivity map. Rays can be followed for multiple reflections within the object scene. Signals having different multiple reflection levels are stored in separate image layers. For evaluating the potentials and limits of the simulation approach, simulated reflectivity maps and distribution maps are compared with real TerraSAR-X images for various complex man-made objects like a skyscraper in Tokyo, the Wynn Hotel in Las Vegas, and the Eiffel Tower in Paris. The results show that the simulation can provide very valuable information to interpret complex SAR images or to predict the reflectivity of planned SAR image acquisitions.

Current traffic research is mostly based on data from fixed-installed sensors like induction loops, bridge sensors, and cameras. Thereby, the traffic flow on main roads can partially be acquired, while data from the major part of the entire road network are not available. Today's optical sensor systems on satellites provide large-area images with 1-m resolution and better, which can deliver complement information to traditional acquired data. In this paper, we present an approach for automatic vehicle detection from optical satellite images. Therefore, hypotheses for single vehicles are generated using adaptive boosting in combination with Haar-like features. Additionally, vehicle queues are detected using a line extraction technique since grouped vehicles are merged to either dark or bright ribbons. Utilizing robust parameter estimation, single vehicles are determined within those vehicle queues. The combination of implicit modeling and the use of a priori knowledge of typical vehicle constellation leads to an enhanced overall completeness compared to approaches which are only based on statistical classification techniques. Thus, a detection rate of over 80% is possible with very high reliability. Furthermore, an approach for movement estimation of the detected vehicle is described, which allows the distinction of moving and stationary traffic. Thus, even an estimate for vehicles' speed is possible, which gives additional information about the traffic condition at image acquisition time.