Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of objects are icebergs drifting in the ocean and can be mistaken for ships in terms of navigation and ocean surveillance. Since these objects can look very much alike in SAR images, the determination of what objects actually are still relies on manual detection and human interpretation. With the increasing interest in the arctic regions for marine transportation, it is crucial to develop novel approaches for automatic monitoring of the traffic in these waters with satellite data. Hence, this study aims at proposing a deep learning model based on YoloV3 for discriminating icebergs and ships, which could be used for mapping ocean objects ahead of a journey. Using dual-polarization Sentinel-1 data, we pilot-tested our approach on a case study in Greenland. Our findings reveal that our approach is capable of training a deep learning model with reliable detection accuracy. Our methodical approach along with the choice of data and classifiers can be of great importance to climate change researchers, shipping industries and biodiversity analysts. The main difficulties were faced in the creation of training data in the Arctic waters and we concluded that future work must focus on issues regarding training data.

中文翻译：

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用于检测和分类海洋物体的深度学习：YoloV3在冰山一船舶识别中的应用

合成孔径雷达（SAR）在海洋监视中发挥着重要作用，无论白天或黑夜，无论云层和极端天气条件如何，它都能够检测漏油，冰山和海上交通。使用SAR探测海洋物体依赖于公认的方法，主要是自适应阈值算法。在大多数水域中，主要的海洋物体是船只，而在北极水域中，绝大多数物体是在海洋中漂流的冰山，就航行和海洋监视而言，它们可能被误认为是船舶。由于这些物体在SAR图像中看起来非常相似，因此确定哪些物体实际上仍取决于人工检测和人工解释。随着北极地区对海上运输的兴趣日益浓厚，开发利用卫星数据自动监控这些水域交通的新颖方法至关重要。因此，本研究旨在提出一种基于YoloV3的深度学习模型，用于区分冰山和船只，该模型可用于在旅程之前绘制海洋物体。使用双极化Sentinel-1数据，我们在格陵兰的案例研究中对我们的方法进行了先导测试。我们的发现表明，我们的方法能够训练具有可靠检测精度的深度学习模型。我们有条不紊的方法以及数据和分类器的选择对于气候变化研究人员，航运业和生物多样性分析师而言可能至关重要。在北极水域创建培训数据时遇到了主要困难，我们得出结论，未来的工作必须集中在与培训数据有关的问题上。