用深度模型融合技术提升雷达图像油污检测精度与稳定性。
SAR-Based Marine Oil Spill Detection Using the DeepSegFusion Architecture
- 结合SegNet与DeepLabV3+，通过注意力机制融合特征。
- 准确率94.85%，交并比0.5685，假警报减少64.4%。
- 适合需要实时监测的海洋环境安全场景。
基于卫星图像的油污检测对环境监控和海上安全至关重要。传统阈值法常因风痕、船尾流等相似现象导致误报率过高。本文提出一种混合深度学习模型DeepSegFusion，用于合成孔径雷达（SAR）图像中的油污分割。该模型融合SegNet与DeepLabV3+，引入基于注意力的特征融合机制，提升边界精度与上下文理解能力。在包含ALOS PALSAR影像的SAR油污数据集上测试，模型达到94.85%的准确率、0.5685的交并比（IoU）以及0.9330的ROC-AUC分数。相比单一基线模型与传统非分割方法，假警报数量减少超三倍，降幅达64.4%。结果表明，DeepSegFusion在多种海况下表现稳定，适用于近实时油污监测场景。
原文摘要 · Abstract (English)
Detection of oil spills from satellite images is essential for both environmental surveillance and maritime safety. Traditional threshold-based methods frequently encounter performance degradation due to very high false alarm rates caused by look-alike phenomena such as wind slicks and ship wakes. Here, a hybrid deep learning model, DeepSegFusion, is presented for oil spill segmentation in Synthetic Aperture Radar (SAR) images. The model uses SegNet and DeepLabV3+ integrated with an attention-based feature fusion mechanism to achieve better boundary precision as well as improved contextual understanding. Results obtained on SAR oil spill datasets, including ALOS PALSAR imagery, confirm that the proposed DeepSegFusion model achieves an accuracy of 94.85%, an Intersection over Union (IoU) of 0.5685, and a ROC-AUC score of 0.9330. The proposed method delivers more than three times fewer false detections compared to individual baseline models and traditional non-segmentation methods, achieving a reduction of 64.4%. These results indicate that DeepSegFusion is a stable model under various marine conditions and can therefore be used in near real-time oil spill monitoring scenarios.
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