In this article, we explore the possibility of detecting polar lows in C-band synthetic aperture radar (SAR) images by means of deep learning. Specifically, we introduce a novel dataset consisting of Sentinel-1 images divided into two classes, representing the presence and absence of a maritime mesocyclone, respectively. The dataset is constructed using the ECMWF reanalysis version 5 (ERA5) dataset as baseline and it consists of 2004 annotated images. To our knowledge, this is the first dataset of its kind to be publicly released. The dataset is used to train a deep learning model to classify the labeled images. Evaluated on an independent test set, the model yields an F1F1 score of 0.95, indicating that polar lows can be consistently detected from SAR images. Interpretability techniques applied to the deep learning model reveal that atmospheric fronts and cyclonic eyes are key features in the classification. Moreover, experimental results show that the model is accurate even if: 1) such features are significantly cropped due to the limited swath width of the SAR; 2) the features are partly covered by sea ice; and 3) land is covering significant parts of the images. By evaluating the model performance on multiple input image resolutions (pixel sizes of 500 m, 1 km, and 2 km), it is found that higher resolution yield the best performance. This emphasizes the potential of using high-resolution sensors like SAR for detecting polar lows, as compared to conventionally used sensors such as scatterometers.

中文翻译：

---

利用深度学习识别 Sentinel-1 SAR 图像中的极地低压


在本文中，我们探讨了通过深度学习检测 C 波段合成孔径雷达 (SAR) 图像中极地低压的可能性。具体来说，我们引入了一个新颖的数据集，该数据集由 Sentinel-1 图像组成，分为两类，分别代表海上中气旋的存在和不存在。该数据集是使用 ECMWF 再分析版本 5 (ERA5) 数据集作为基线构建的，由 2004 年带注释的图像组成。据我们所知，这是第一个公开发布的此类数据集。该数据集用于训练深度学习模型以对标记图像进行分类。在独立测试集上进行评估，该模型的 F1F1 得分为 0.95，表明可以从 SAR 图像中一致地检测到极地低压。应用于深度学习模型的可解释性技术表明，大气锋和气旋眼是分类中的关键特征。此外，实验结果表明，即使在以下情况下，该模型也是准确的：1）由于SAR测绘带宽度有限，这些特征被显着裁剪； 2) 地物部分被海冰覆盖； 3) 土地覆盖了图像的重要部分。通过评估多个输入图像分辨率（像素大小为 500 m、1 km 和 2 km）的模型性能，发现较高的分辨率会产生最佳性能。与传统使用的传感器（如散射仪）相比，这强调了使用 SAR 等高分辨率传感器来检测极地低压的潜力。