高分辨率海冰拓扑结构分析：基于弱标签SAR影像的解析对数缩放方法
Analytical Logit Scaling for High-Resolution Sea Ice Topology Retrieval from Weakly Labeled SAR Imagery
AI总结：
本文提出一种弱监督深度学习方法，结合SAR与AMSR-2数据，通过解析对数缩放技术实现高分辨率海冰拓扑结构提取，准确率达78%。
摘要：
高分辨率海冰制图对于北极导航和气候监测至关重要。然而，操作冰图仅提供粗略的区域多边形（弱标签），迫使自动分割模型难以实现像素级精度，通常产生不自信的模糊浓度图。本文提出一种弱监督深度学习管道，利用U-Net架构融合Sentinel-1 SAR和AMSR-2辐射数据，并使用基于区域的损失进行训练。为克服弱标签导致的严重不自信问题，我们引入了解析对数缩放方法，在推理后应用。通过动态计算每个场景潜在空间的百分位数（2%和98%）的温度和偏置，我们强制预测的物理二值化。这种自适应缩放充当拓扑提取器，成功在40米分辨率下揭示细粒度的海冰裂缝（冰裂隙），无需任何手动像素级注释。我们的方法不仅解决了局部拓扑问题，还完美保留了区域宏观浓度，实现了在高度破碎的夏季场景中78%的准确率，从而弥合了弱监督学习与高分辨率物理分割之间的差距。
英文摘要：
High-resolution sea ice mapping using Synthetic Aperture Radar (SAR) is crucial for Arctic navigation and climate monitoring. However, operational ice charts provide only coarse, region-level polygons (weak labels), forcing automated segmentation models to struggle with pixel-level accuracy and often yielding under-confident, blurred concentration maps. In this paper, we propose a weakly supervised deep learning pipeline that fuses Sentinel-1 SAR and AMSR-2 radiometry data using a U-Net architecture trained with a region-based loss. To overcome the severe under-confidence caused by weak labels, we introduce an Analytical Logit Scaling method applied post-inference. By dynamically calculating the temperature and bias based on the latent space percentiles (2\% and 98\%) of each scene, we force a physical binarization of the predictions. This adaptive scaling acts as a topological extractor, successfully revealing fine-grained sea ice fractures (leads) at a 40-meter resolution without requiring any manual pixel-level annotations. Our approach not only resolves local topology but also perfectly preserves regional macroscopic concentrations, achieving a 78\% accuracy on highly fragmented summer scenes, thereby bridging the gap between weakly supervised learning and high-resolution physical segmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
