Institute of Estuarine and Coastal Research, School of Ocean Engineering and Technology, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China.
Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China; Collaborative Innovation Center for Natural Resources Planning and Marine Technology of Guangzhou, Guangzhou 510060, China.
Mar Pollut Bull. 2024 Sep;206:116777. doi: 10.1016/j.marpolbul.2024.116777. Epub 2024 Jul 30.
This study evaluates the performance of three typical convolutional neural network based deep learning algorithms for oil spill detection using medium-resolution optical satellite imagery from Sentinel-2 MSI, Landsat-8 OLI, and Landsat-9 OLI2. Oil slick training and validation dataset were created through a semi-automatic labeling approach, based on chronic and accidental oil spill cases reported worldwide. The research enhances UNet, BiSeNetV2, and DeepLabV3+ architectures by integrating attention mechanisms including the Squeeze-and-Excitation module (SE), Convolutional Block Attention Module (CBAM), and a Simple, parameter-free Attention Module (SimAM), analyzing the optimal model for oil spill detection. Notably, UNet integrated with CBAM, especially with sun glint as a feature, significantly outperformed others, achieving a micro-average F1 score of 88.8 %. This research highlights deep learning's potential in optical remote sensing for oil spill detection, stressing its escalating relevance with the growing deployment of medium- to high-resolution optical satellites.
本研究评估了三种基于典型卷积神经网络的深度学习算法在使用 Sentinel-2 MSI、Landsat-8 OLI 和 Landsat-9 OLI2 中分辨率光学卫星图像进行溢油检测方面的性能。通过基于全球报告的慢性和意外溢油案例的半自动标记方法创建了油膜训练和验证数据集。该研究通过集成注意力机制（包括 squeeze-and-excitation 模块（SE）、卷积块注意力模块（CBAM）和简单、无参数注意力模块（SimAM））增强了 UNet、BiSeNetV2 和 DeepLabV3+架构，分析了溢油检测的最优模型。值得注意的是，集成了 CBAM 的 UNet，特别是将太阳耀斑作为特征，表现明显优于其他模型，其微平均 F1 得分为 88.8%。本研究强调了深度学习在光学遥感溢油检测中的潜力，随着中高分辨率光学卫星的日益部署，凸显了其相关性的不断提升。
