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不可或缺的组成部分。随着其广泛应用导致的需求增长，意外溢油事故难以避免。尽管溢油清理本身困难重重，但首要挑战在于溢油检测。本研究测试了可有效训练以检测溢油的深度编码器-解码器模型的可行性。研究对比了多种分割模型在高维卫星合成孔径雷达（SAR）图像数据上的表现，实验采用了多种模型组合。性能最佳的模型采用ResNet-50编码器与DeepLabV3+解码器，其平均交并比（IoU）达到64.868%，"溢油"类别IoU为61.549%。作为对比，当前基准模型的平均IoU为65.05%，"溢油"类别IoU为53.38%。