High-resolution Synthetic Aperture Radar (SAR) data represent an essential resource for the extraction of Ground Control Points (GCP) with sub-metric accuracy without in situ measurement campaigns. Conceptually, SAR-based GCP extraction consists of the following two steps: (i) identification of the same local feature on more SAR images and determination of their range/azimuth coordinates; (ii) spatial 3D positioning retrieval from the 2D radar coordinates, through spatial triangulation (stereo analysis) and inversion methods. In order to boost the geolocation accuracy, SAR images must be acquired from different line of sights, with intersection angles typically wider than 10 degrees, or even in opposite looking directions. In the present study, we present an algorithm specifically designed for ensuring robustness and accuracy in the fully automatic detection of bright isolated targets (steel light poles or towers) even when dealing with opposite looking data takes. In particular, the popular Harris algorithm has been selected as detector because it is the most stable and robust-to-noise algorithm for corners detection on SAR images. We outline the designed algorithmic solution and discusses the results derived over the urban area of Pisa (Italy), where more than ten COSMO-SkyMed Enhanced Spotlight (ES) stereo images are available, thus resulting an optimal test site for an assessment of the performances of the processing chain. The experimental analysis proofs that, assumed timing has been properly recalibrated, we are capable to automatically extract GCP from CSK ES data takes consisting in a very limited number of images.