Generalization in Object Recognition from SAR Imagery

Abstract

Object recognition in synthetic aperture radar images is a well studied topic that has gained a significant amount of attention within the last decades. Modern approaches are based on machine learning, i.e. deep learning, and often show excellent performance. What is so far missing in the literature is a study dedicated to the generalization capabilities of object recognition approaches, i.e. how well a given system can be transferred to new and previously unseen data. In this paper, the proposed recognition model is trained and tested on a unique dataset of 25 high-resolution TerraSAR-X images (X-band), acquired over four different airports in Staring Spotlight mode. We show how classification performance changes for different application scenarios which require different training and evaluation setups.