The consistent and long-term spaceborne synthetic aperture radar (SAR) missions such as Sentinel-1 (S-1) provide high-quality dual-polarized C-band images particularly suited to sea ice monitoring. SAR data are currently the primary source of information for sea ice charting made by human ice experts as a result of its integration with multiple information sources at different scales (mainly radiometers). The rise of deep learning now opens the prospect of automatic sea ice mapping. In this article, we investigate the potential of a fully convolutional network (FCN) for the automatic estimation of sea ice concentration (SIC). With input data down-sampled at 200 m and an FCN architecture (depth and receptive field) duly parameterized, our approach is to mimic the work of an analyst who considers general context and does not necessarily use the highest possible resolution but speckle-noise polluted data. A comprehensive database is generated with 1320 dual-polarized S-1 scenes collocated with ice charts produced by MET Norway. A dedicated attention is paid to seasonal representativeness to ensure the adequate performance for all sea ice types. Even if the FCN output is modeled as a categorical problem, the proposed architecture accounts for the semantic distances between SIC classes by introducing an auxiliary loss. A comparative benchmark with Ocean and Sea Ice Satellite Application Facility (OSISAF) and MET Norway SIC products is carried out, showing an overall accuracy of 78.2% for our 6-class classification approach. The FCN model is shown to be evenly robust to sea ice seasonal variability and incidence angle.

中文翻译：

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基于全卷积网络的 Sentinel-1 SAR 图像分类海冰浓度预测


Sentinel-1 (S-1) 等一致且长期的星载合成孔径雷达 (SAR) 任务可提供高质量的双偏振 C 波段图像，特别适合海冰监测。由于SAR数据与不同尺度的多个信息源（主要是辐射计）集成，目前SAR数据是人类冰图绘制的主要信息来源。深度学习的兴起开启了自动海冰测绘的前景。在本文中，我们研究了全卷积网络（FCN）在自动估计海冰浓度（SIC）方面的潜力。输入数据在 200 m 处下采样并且 FCN 架构（深度和感受野）适当参数化，我们的方法是模仿分析人员的工作，该分析人员考虑一般背景，不一定使用尽可能高的分辨率，但会受到散斑噪声的影响数据。综合数据库由 1320 个双偏振 S-1 场景与 MET 挪威制作的冰图搭配而成。我们特别关注季节性代表性，以确保所有海冰类型都有足够的性能。即使 FCN 输出被建模为分类问题，所提出的架构也会通过引入辅助损失来解释 SIC 类别之间的语义距离。与海洋和海冰卫星应用设施 (OSISAF) 和挪威 MET SIC 产品进行了比较基准，结果显示我们的 6 类分类方法的总体准确度为 78.2%。 FCN 模型对海冰季节性变化和入射角具有同样的鲁棒性。