Adaptive Refocusing Chain for Moving Ships in Satellite SAR Images

In this study, an adaptive refocusing scheme for moving ships in satellite synthetic aperture radar (SAR) images is proposed to cope with various types of motions of ship targets. To decide the type of ship's motion, the phase signals of principal scatterers are analyzed based on the inverse SAR (ISAR) signal model with the help of a joint time–frequency transform and deep learning model. Then, proper ISAR-based refocusing algorithms are used to generate a well-focused image considering the ship's motion. The design of the adaptive refocusing concept enables us to select appropriate algorithms to retrieve the exact scattering mechanisms of ship targets. In addition, to cope with defocusing due to the complex 3-D motion of the ship, an efficient reconstruction strategy based on compressive sensing is devised. It is a concept different from conventional optimal time windowing, which deals with the complex motion of the ship target, and it yields a well-focused image that retains the spatial resolution of the original ship image. In experiments using simulated and real SAR images, the proposed method shows reliable refocusing results for various ship targets compared to traditional methods.