论文标题
通过子术的深度学习指导：SAR图像的海洋模式
Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery
论文作者
论文摘要
Spaceborne合成孔径雷达几乎可以在几乎所有天气条件下白天或黑夜提供仪表尺度的图像。这使其成为许多地球物理应用的独特资产。 Sentinel 1 SAR波模式的小插曲已成为自2014年以来捕获许多重要的海洋和大气现象的可能性。但是，考虑到所提供的数据量，扩展的申请需要一种策略来自动处理和提取地球物理参数。在这项研究中，我们建议将亚术分解作为SAR深度学习模型的预处理阶段。我们的数据居中方法超过了基线0.7，在TenGeOpsArwv数据集上获得了最新技术。此外，我们从经验上表明，亚术分解可以通过增加无监督分割方法的簇数来带来原始小插图的其他信息。总体而言，我们鼓励开发数据中心方法，这表明数据预处理可能会对现有深度学习模型带来重大的性能改善。
Spaceborne synthetic aperture radar can provide meters scale images of the ocean surface roughness day or night in nearly all weather conditions. This makes it a unique asset for many geophysical applications. Sentinel 1 SAR wave mode vignettes have made possible to capture many important oceanic and atmospheric phenomena since 2014. However, considering the amount of data provided, expanding applications requires a strategy to automatically process and extract geophysical parameters. In this study, we propose to apply subaperture decomposition as a preprocessing stage for SAR deep learning models. Our data centring approach surpassed the baseline by 0.7, obtaining state of the art on the TenGeoPSARwv data set. In addition, we empirically showed that subaperture decomposition could bring additional information over the original vignette, by rising the number of clusters for an unsupervised segmentation method. Overall, we encourage the development of data centring approaches, showing that, data preprocessing could bring significant performance improvements over existing deep learning models.
