Detection and Velocity Estimation of Moving Vehicles in High-Resolution Spaceborne Synthetic Aperture Radar Data Stefan Hinz, Diana Weihing, Steffen Suchandt, Richard Bamler Remote Sensing Technology, TU Muenchen, Germany stefan.hinz@bv.tum.de Abstract — Automatic estimation of traffic parameters has evolved to an important topic of research. Current and upcoming SAR satellite missions offer new possibilities for traffic monitoring and control from space as an alternative to conventional traffic data acquisition. In this paper a detection approach is presented which evaluates simultaneously the effects moving objects suffer from in the SAR focusing process. Information about the measured signal and the expected signal are utilized in the detection framework. Analyses of the proposed technique are done with real spaceborne SAR data. I. INTRODUCTION 1.1 Motivation Increasing traffic has an influence on urban and suburban planning. Usually traffic models are utilized to predict traffic and forecast transportation. To derive statistical parameters of traffic for these models, data of large areas acquired at any time is desirable. Therefore, spaceborne SAR missions can be a solution for this aim. With the new TerraSAR-X (Roth, 2003) or RADARSAT-2 mission, SAR images up to 1 m resolution are available. Additionally, the Dual Receive Antenna (DRA) mode enables the reception of two SAR images of the same scene within a small timeframe, which can be utilized for along-track interferometry. 1.2 Related work Since moving objects suffer from special effects in the SAR processing algorithm, specific methods for detecting vehicles are required. In several publications the task of detecting moving vehicles has been treated. In military research this problem is known as Ground Moving Object Indication (GMTI). If more than two channels are available the use of Space Time Adaptive Processing (STAP) is the optimal method (Ender, 1999; Klemm, 1998). In the case of two- channel systems, like TerraSAR-X or the Canadian RADARSAT-2, interferometric approaches can be used for detecting vehicles. Along-Track Interferometry (ATI) (Sikaneta and Gierull, 2005) and the so-called Displaced Phase Center Array (DPCA) method are the classical methods to do so. They can be regarded as an approximation of the limiting case of STAP. In the ATI technique an interferogram is formed from the two SAR images by complex conjugate multiplication, whereas in the DPCA processing the two calibrated SAR images are subtracted from each other. The interferometric phase in ATI and the magnitude of the result in DPCA are evaluated for detection (Gierull, 2002). These detections are done based on a constant false alarm rate scheme. In (Meyer et al., 2006) these approaches have been extended by integrating apriori information, GIS data of road networks. However, e.g. ATI can only be applied if the motion of the vehicle affects the interferometric phase, which is not the case if vehicles are moving in along-track direction. To estimate ground moving parameters for vehicles travelling in along-track, one method is to apply filterbanks with differently designed matched filters (Gierull and Sikaneta, 2004; Weihing et al., 2006). The presented detection approach in this paper considers simultaneously the effects in SAR images, which are caused by the vehicle’s motion in across- and along-track. It furthermore can include multi-temporal SAR data. The scheme is derived from statistical detection theory and its principle relies on comparing an expected signal with the actual measurement. Therefore, different information is combined in this detection algorithm. The expected signal, the measured signal and their variances are included to decide whether a vehicle is present or not. In the next section a short summary is given of the different effects in SAR images caused by the vehicle’s motion. The proposed detection scheme is explained in Sect. 3. Afterwards the performance of this detector is analyzed using experimental TerraSAR-X data in Sect. 4. II. MOVING OBJECTS IN SAR DATA – A SUMMARY In an air- or spaceborne SAR imaging process a Radar scans the earth in a side-looking fashion during its flight over the scene. While the sensor is moving it transmits microwave pulses at constant intervals given by the Pulse Repetition Frequency (PRF) and receives the echoes. In Fig. 1 the geometry of an image acquisition is shown. The radar is flying in a certain altitude h along the x-axis, also referred to azimuth direction or alongtrack. The y-axis, which is oriented perpendicular to the flight path, is usually refered to as ground range or across-track direction. The position of the sensor at a certain point of time is given by P sat (t) = [x sat (t), y sat (t), z sat (t)] and the location of the moving target by P target = [x mover (t), y mover (t), z mover (t)]. The distance between sensor and target corresponds to: 978-1-4244-2340-8/08/$25.00 ©2008 IEEE