In this paper, we explore the possibility of detecting polar lows in C-band 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 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 F-1 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: (i) such features are significantly cropped due to the limited swath width of the SAR, (ii) the features are partly covered by sea ice and (iii) land is covering significant parts of the images. By evaluating the model performance on multiple input image resolutions (pixel sizes of 500m, 1km and 2km), it is found that higher resolution yield the best performance. This emphasises the potential of using high resolution sensors like SAR for detecting polar lows, as compared to conventionally used sensors such as scatterometers.
翻译:在本文中,我们探索了通过深层学习探测C波段合成孔径雷达图像中极低点的可能性。 具体地说, 我们引入了由Sentinel-1图像分为两类的新数据集, 分别代表海洋中子环球的存在和缺乏。 数据集是使用ERA5数据集构建的, 由2004年附加说明的图像组成。 据我们所知, 这是这类数据集中第一个要公开发布的数据集。 该数据集用来训练一个深层学习模型来分类标签图像。 在独立测试集中,模型得出F-1分0.95, 表明可以从合成孔径雷达图像中持续检测极低点。 用于深层学习模型的可解释性能技术显示大气前沿和环球眼是分类中的关键特征。 此外, 实验结果显示,即使:(一) 由于合成孔径雷达的宽度有限,这些特征被大量分解。 (二) 这些特征部分由海冰覆盖, (三) 陆地覆盖图像的重要部分。 通过对模型性能进行评估, 将高分辨率1的分辨率作为高分辨率的分辨率的模型和高分辨率的分辨率,, 将模型作为高分辨率的分辨率的分辨率的分辨率的SAR1比。