Marine oil spills are urgent environmental hazards that demand rapid and reliable detection to minimise ecological and economic damage. While Synthetic Aperture Radar (SAR) imagery has become a key tool for large-scale oil spill monitoring, most existing detection methods rely on deep learning-based segmentation applied to single SAR images. These static approaches struggle to distinguish true oil spills from visually similar oceanic features (e.g., biogenic slicks or low-wind zones), leading to high false positive rates and limited generalizability, especially under data-scarce conditions. To overcome these limitations, we introduce Oil Spill Change Detection (OSCD), a new bi-temporal task that focuses on identifying changes between pre- and post-spill SAR images. As real co-registered pre-spill imagery is not always available, we propose the Temporal-Aware Hybrid Inpainting (TAHI) framework, which generates synthetic pre-spill images from post-spill SAR data. TAHI integrates two key components: High-Fidelity Hybrid Inpainting for oil-free reconstruction, and Temporal Realism Enhancement for radiometric and sea-state consistency. Using TAHI, we construct the first OSCD dataset and benchmark several state-of-the-art change detection models. Results show that OSCD significantly reduces false positives and improves detection accuracy compared to conventional segmentation, demonstrating the value of temporally-aware methods for reliable, scalable oil spill monitoring in real-world scenarios.

翻译：海洋溢油是紧迫的环境危害，需要快速可靠的检测以最大限度减少生态和经济损失。虽然合成孔径雷达（SAR）图像已成为大规模溢油监测的关键工具，但现有检测方法大多依赖于对单幅SAR图像应用基于深度学习的分割技术。这些静态方法难以区分真实溢油与视觉相似的海面特征（如生物油膜或低风区），导致高误报率和有限的泛化能力，在数据稀缺条件下尤为明显。为克服这些局限性，我们提出溢油变化检测（OSCD）这一新的双时相任务，专注于识别溢油前后SAR图像间的变化。由于真实配准的溢油前图像并非总是可用，我们提出时序感知混合修复（TAHI）框架，该框架可从溢油后SAR数据生成合成的溢油前图像。TAHI整合两个关键组件：用于无油重建的高保真混合修复，以及用于辐射和海况一致性的时序真实性增强。利用TAHI，我们构建了首个OSCD数据集并对多种先进变化检测模型进行基准测试。结果表明，与传统分割方法相比，OSCD显著降低了误报率并提升了检测精度，这证明了时序感知方法在实际场景中实现可靠、可扩展溢油监测的价值。