Synthetic aperture radar (SAR) images are currently widely used in target recognition tasks. In this work, we propose an automatic approach for radar shadow detection and extraction from SAR images utilizing geometric projections along with the digital elevation model (DEM), which corresponds to the given georeferenced SAR image. First, the DEM is rotated into the radar geometry, so that each row would match that of a radar line of sight. Next, we extract the shadow regions by processing row by row until the image is covered fully. We test the proposed shadow detection approach on different DEMs and simulated one-dimensional signals and two-dimensional hills and valleys modeled by various variance-based Gaussian functions. Experimental results indicate that the proposed algorithm produces good results in detecting shadows in SAR images with high resolution.