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With the launch of the German TerraSAR-X system in June 2007, a new generation of high-resolution spaceborne synthetic aperture radar (SAR) data is available, which should facilitate the interpretation of urban environments. Our overall objective in this letter is to provide a semiautomatic tool for urban area interpretation using SAR data. We propose in this letter to fuse different automatic object extractors in order to provide more reliable pieces of interpretation. Our fusion is a coarse-to-fine approach. First, a segmentation of the image is performed to partition the scene into regions having similar properties. The second step consists in detecting bright and dark linear structures which are, in general, linked to the presence of buildings and roads (main classes in urban areas), respectively. The last step gives the final image interpretation using contextual knowledge. Evaluation of the proposed approach in mapping urban areas was carried out using real TerraSAR-X data over the city of Las Vegas in the U.S.