Extraction of Small Objects from Ground-based Multi-static SAR Images using CFAR Algorithm with Generalized Gamma Distribution

Ground-based multi-static synthetic aperture radar is able to produce high-resolution images with sufficient clutter suppression. It is therefore capable of detecting even small and weakly reflecting objects. The automatic extraction of these targets is of great interest for many applications like efficient landmine detection, for instance. Furthermore the automatic extraction of targets is the first useful stage for automatic target recognition. A Constant False Alarm Rate (CFAR) algorithm is a suitable approach for this task by analyzing the distribution of the surrounding clutter and calculating a reasonable threshold for each pixel. The Generalized Gamma distribution forms a large class of distributions and is therefore suitable to model different kind of clutter. In this paper the selected and implemented algorithm is validated using near-range high-resolution data from different kind of objects.