Abstract. The ocean surface exhibits a variety of oceanic and atmospheric phenomena. Automatically detecting and identifying these phenomena is crucial for understanding oceanic dynamics and ocean-atmosphere interactions. In this study, we select 2,383 Sentinel-1 WV mode images and 2,628 IW mode sub-images to construct a semantic segmentation dataset that includes 12 typical oceanic and atmospheric phenomena. Each phenomenon is represented by approximately 400 sub-images, resulting in a total of 5,011 images. The images in this dataset have a resolution of 100 meters and dimensions of 256 × 256 pixels. We propose a modified Segformer model to segment semantically these multiple categories of oceanic and atmospheric phenomena. Experimental results show that the modified Segformer model achieves an average Dice coefficient of 80.98 %, an average IoU of 70.32 %, and an overall accuracy of 87.13 %, demonstrating robust segmentation performance of typical oceanic and atmospheric phenomena in SAR images.

中文翻译：

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典型海洋和大气现象的SAR图像语义分割


摘要。海洋表面呈现出各种海洋和大气现象。自动检测和识别这些现象对于理解海洋动力学和海洋-大气相互作用至关重要。在本研究中，我们选择了 2,383 张 Sentinel-1 WV 模式图像和 2,628 张 IW 模式子图像来构建语义分割数据集，其中包括 12 种典型的海洋和大气现象。每个现象由大约 400 个子图像表示，总共 5,011 个图像。该数据集中的图像分辨率为 100 米，尺寸为 256 × 256 像素。我们提出了一种改进的 Segformer 模型，用于对海洋和大气现象的多个类别进行语义分割。实验结果表明，改进后的Segformer模型平均Dice系数为80.98%，平均IoU为70.32%，总体精度为87.13%，对SAR图像中典型海洋和大气现象具有良好的分割性能。