Beyond Segmentation: An Oil Spill Change Detection Framework Using Synthetic SAR Imagery

Chenyang Lai; Shuaiyu Chen; Tianjin Huang; Siyang Song; Guangliang Cheng; Chunbo Luo; Zeyu Fu

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arXiv · 2601.02139

Beyond Segmentation: An Oil Spill Change Detection Framework Using Synthetic SAR Imagery

Chenyang Lai·Shuaiyu Chen·Tianjin Huang·Siyang Song·Guangliang Cheng·Chunbo Luo·Zeyu Fu

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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.

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Chenyang Lai, Shuaiyu Chen, Tianjin Huang, Siyang Song, Guangliang Cheng, Chunbo Luo, Zeyu Fu. 2026-01-05. Beyond Segmentation: An Oil Spill Change Detection Framework Using Synthetic SAR Imagery. https://arxiv.org/abs/2601.02139

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