A Review of Deep-Learning-Based SAR Image Ship Interpretation Technology: The Latest Advances

Synthetic aperture radar (SAR) has played a crucial role in ship monitoring, maritime rescue, and other fields due to its ability to operate around the clock and in all weather conditions. The interpretation and analysis of ship targets in SAR images have always been an important research direction in the field of remote sensing. In recent years, with the application of deep learning technology to the field of SAR image interpretation, an increasing number of deep-learning-based ship interpretation methods have been proposed and have achieved many new results in various tasks such as ship detection, identification, parameter estimation, and others. Against this backdrop, it is necessary to review the recently proposed deep-learning-based SAR image ship interpretation technologies. This paper reviews English journal and conference articles published from January 2022 to May 2025. First, we provide a detailed organization and statistical analysis of the commonly used and newly released public datasets in this field. Then, focusing on the ship detection and identification task, which has the highest research interest and the largest number of research papers, we innovatively categorize the methods into two classes based on whether they consider the characteristics of SAR image: deep-learning-based methods directly transferred from optical and the methods specifically designed for the characteristics of SAR images. Subsequently, we sorts out and summarize the new tasks and challenges proposed in the field of SAR image ship interpretation, such as cross-domain SAR image ship detection and recognition methods, ship detection with incomplete SAR imaging data, ship wake detection, ship parameter estimation, and three-dimensional ship refocusing. Finally, we summarize the current research status of this field and speculates on possible future research directions.