This paper proposes a useful scheme to augment the amount of training database (DB) for ship detection using Korea multi-purpose satellite-5 (KOMPSAT-5) images and deep learning. The proposed scheme utilizes electromagnetic numerical analysis technique to generate SAR chips of ship targets. In addition, two sea clutter models are adopted to simulate realistic SAR patches containing various SAR chips. Then, the simulated SAR patches are directly used to train single shot multi-box detector (SSD) model, leading to the improvement of ship detection performance.