论文标题
SAD：在合成孔径雷达图像中进行机场检测的大规模数据集
SAD: A Large-scale Dataset towards Airport Detection in Synthetic Aperture Radar Images
论文作者
论文摘要
机场在军事和民用领域都起着重要作用。近年来，基于合成的孔径雷达（SAR）机场检测受到了越来越多的关注。但是，由于SAR成像和注释过程的高成本，没有公开可用的SAR数据集用于机场检测。结果，深度学习方法尚未在机场检测任务中充分使用。为了提供SAR图像中机场检测研究的基准，本文介绍了一个大型SAR机场数据集（SAD）。为了充分反映现实世界应用的需求，它包含来自Sentinel 1B的624张SAR图像，并涵盖了具有不同尺度，方向和形状的104个机场实例。该数据集多种深度学习方法的实验证明了其有效性。它开发了最新的机场检测算法或其他相关任务。
Airports have an important role in both military and civilian domains. The synthetic aperture radar (SAR) based airport detection has received increasing attention in recent years. However, due to the high cost of SAR imaging and annotation process, there is no publicly available SAR dataset for airport detection. As a result, deep learning methods have not been fully used in airport detection tasks. To provide a benchmark for airport detection research in SAR images, this paper introduces a large-scale SAR Airport Dataset (SAD). In order to adequately reflect the demands of real world applications, it contains 624 SAR images from Sentinel 1B and covers 104 airfield instances with different scales, orientations and shapes. The experiments of multiple deep learning approach on this dataset proves its effectiveness. It developing state-of-the-art airport area detection algorithms or other relevant tasks.
