Crude oil is an integral component of the modern world economy. With the growing demand for crude oil due to its widespread applications, accidental oil spills are unavoidable. Even though oil spills are in and themselves difficult to clean up, the first and foremost challenge is to detect spills. In this research, the authors test the feasibility of deep encoder-decoder models that can be trained effectively to detect oil spills. The work compares the results from several segmentation models on high dimensional satellite Synthetic Aperture Radar (SAR) image data. Multiple combinations of models are used in running the experiments. The best-performing model is the one with the ResNet-50 encoder and DeepLabV3+ decoder. It achieves a mean Intersection over Union (IoU) of 64.868% and a class IoU of 61.549% for the "oil spill" class when compared with the current benchmark model, which achieved a mean IoU of 65.05% and a class IoU of 53.38% for the "oil spill" class.
翻译：原油是现代世界经济中不可或缺的组成部分。由于其广泛的应用，对原油的需求不断增长，因此意外的油污泄漏是不可避免的。即使油污本身难以清理，但首要的挑战是检测油污泄漏。在这项研究中，作者测试了使用深度编码器-解码器模型，能够有效地训练以侦测油污泄漏的可行性。该工作比较了在高维卫星合成孔径雷达（SAR）图像数据上运行的多个分割模型的结果。在运行实验时使用了多个模型组合。最佳表现模型是使用 ResNet-50 编码器和 DeepLabV3+ 解码器所得到的模型，当与当前基准模型进行比较时，平均交叉比对（IoU）为 64.868%，油污类交叉比对为 61.549%。当前基准模型平均 IoU 为 65.05%，油污类 IoU 为 53.38%。
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