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

• URL: http://arxiv.org/abs/2601.02139v1
• Date: Mon, 05 Jan 2026 14:10:13 GMT
• Title: Beyond Segmentation: An Oil Spill Change Detection Framework Using Synthetic SAR Imagery
• Authors: Chenyang Lai, Shuaiyu Chen, Tianjin Huang, Siyang Song, Guangliang Cheng, Chunbo Luo, Zeyu Fu,
• Abstract summary: Oil Spill Change Detection (OSCD) is a new bi-temporal task that focuses on identifying changes between pre- and post-spill SAR images.<n>Results show that OSCD significantly reduces false positives and improves detection accuracy compared to conventional segmentation.
• Score: 36.496017161452215
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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