Simulated SAR multi-vehicle scene dataset generation for deep learning-based surveillance systems

Abstract

In this paper, we propose a comprehensive approach for the effective simulation of multi-object SAR imagery dataset generation using IRIS Electromagnetic modeling and simulation system – called IRIS-EM. Further, we describe our methodology for systematic generation of large-scale simulated SAR datasets of multi-domain (air, ground, sea) in multi-object scenes. Different from our earlier work, in this study, we considered the impact of having multiple objects in the same scene. We discuss the challenges associated with generating simulated SAR imagery datasets for multi-vehicle for the training of DL-based surveillance systems. Lastly, we present novel automation techniques ensuring realistic multi-vehicle placement in a test scene while sustaining real-world representation with fidelity.