Bridging optical and SAR images via semantic prompt-guided progressive alignment for rotated cross-domain ship detection

Ship detection in remote sensing imagery is essential for diverse maritime-related tasks, including ocean surveillance, fisheries management, and environmental assessment. In operational scenarios, optical imagery provides rich texture cues under clear conditions, whereas synthetic aperture radar (SAR) enables reliable observation in nighttime and cloudy weather. However, cross-domain ship detection across optical and SAR modalities is still challenging due to discrepancies in imaging mechanisms, speckle noise, and background clutter, particularly in near-shore scenarios with similar reflection characteristics, together with the arbitrariness of ship orientation. To address these issues, we propose RotCD-Ship, a rotated cross-domain ship detection framework that bridges the domain gap between optical and SAR images while enabling accurate detection of arbitrarily oriented ships. Specifically, a domain knowledge-guided semantic prompt (DKSP) strategy based on SAR physical priors is introduced to suppress background clutter such as ship wakes and coastal interference. To handle modal divergence, we design a progressive feature alignment scheme that combines multi-scale local feature alignment (MSL-align) and global feature alignment (GF-align), enabling transfer of both fine-grained textures and high-level semantics across domains. Furthermore, a coarse-to-fine rotated region of interest (CF-RRoI) generator is developed to enhance localization precision of strip-like ships in SAR images by progressively refining orientation-aware proposals. Extensive evaluations on five public ship detection datasets show that RotCD-Ship significantly outperforms state-of-the-art methods in both accuracy and robustness, achieving an average mAP improvement of 7.5% in the horizontal ship detection task and 5.5% in the oriented ship detection task compared to the best existing methods. In addition, large-scale tests on Gaofen-3 SAR images further verify the strong generalization in dense-ship and complex coastal environments, highlighting the practical applicability of our framework for all-weather maritime monitoring.