论文标题
从深度学习的SAR图像中大规模检测和分类漏油
Large-scale detection and categorization of oil spills from SAR images with deep learning
论文作者
论文摘要
我们提出了一个深度学习框架，以大规模检测和分类合成孔径雷达（SAR）图像中的漏油。通过经过精心设计的神经网络模型，用于在广泛的数据集中训练的图像分割，我们能够获得漏油检测的最新性能，从而实现了与人类运营商产生的结果相当的结果。我们还引入了一项分类任务，该任务在SAR中的漏油事件中是新颖的。具体而言，在被检测到的情况下，每个漏油事件也根据与其形状和纹理特征有关的不同类别进行分类。分类结果为通过世界领先的提供商改善溢油服务的设计提供了宝贵的见解。作为最后的贡献，我们介绍了我们的运营管道和大规模数据的可视化工具，该工具允许检测和分析全球漏油事件的历史存在。
We propose a deep learning framework to detect and categorize oil spills in synthetic aperture radar (SAR) images at a large scale. By means of a carefully designed neural network model for image segmentation trained on an extensive dataset, we are able to obtain state-of-the-art performance in oil spill detection, achieving results that are comparable to results produced by human operators. We also introduce a classification task, which is novel in the context of oil spill detection in SAR. Specifically, after being detected, each oil spill is also classified according to different categories pertaining to its shape and texture characteristics. The classification results provide valuable insights for improving the design of oil spill services by world-leading providers. As the last contribution, we present our operational pipeline and a visualization tool for large-scale data, which allows to detect and analyze the historical presence of oil spills worldwide.
