Abstract
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.
Abstract (translated)
海洋石油泄漏是紧迫的环境危害，需要快速且可靠的检测以减少生态和经济损失。尽管合成孔径雷达（SAR）图像已成为大规模油污监测的关键工具，但大多数现有的检测方法依赖于应用于单张SAR图像的基于深度学习的分割技术。这些静态方法难以区分真正的油污与视觉上相似的海洋特征（例如生物性油膜或低风区），导致较高的误报率和较低的泛化能力，尤其是在数据稀缺的情况下尤为明显。 为了克服这些限制，我们引入了油污变化检测（OSCD），这是一种新的双时相任务，重点是识别前后SAR图像中的变化。由于实际配准的前泄露图像并不总是可用，我们提出了时空感知混合修复框架（TAHI），该框架从后泄露SAR数据中生成合成的前泄漏图像。TAHI整合了两个关键组件：高保真度混合修复用于无油重建和时间现实增强以实现辐射特性和海况的一致性。 利用TAHI，我们构建了第一个OSCD数据集，并对几种最先进的变化检测模型进行了基准测试。结果表明，与传统的分割方法相比，OSCD显著降低了误报率并提高了检测准确性，证明了时空感知方法在真实场景中可靠、大规模监测石油泄漏的价值。
URL
https://arxiv.org/abs/2601.02139
