用弱标签数据实现40米分辨率海冰裂隙精准提取
Analytical Logit Scaling for High-Resolution Sea Ice Topology Retrieval from Weakly Labeled SAR Imagery
- 融合SAR与辐射计数据，基于区域损失训练U-Net模型
- 动态调整预测置信度，实现78%高碎片夏季场景准确率
- 无需像素标注，适合北极导航与气候监测应用
利用合成孔径雷达（SAR）进行高分辨率海冰制图对北极航行和气候监测至关重要。然而，现有冰图仅提供粗粒度区域多边形（弱标签），导致自动分割模型难以达到像素级精度，常生成低置信度、模糊的浓度图。本文提出一种弱监督深度学习流程，融合Sentinel-1 SAR与AMSR-2辐射计数据，采用基于区域损失训练的U-Net架构。为克服弱标签带来的严重置信度不足问题，引入解析式对数缩放方法，在推理后动态计算温度与偏置，基于每景图像潜在空间的2%与98%分位数，强制预测结果物理二值化。该自适应缩放作为拓扑提取器，成功在40米分辨率下揭示细粒度海冰裂缝（裂隙），无需任何人工像素级标注。本方法不仅还原局部拓扑结构，还完美保留区域宏观浓度特征，在高度破碎的夏季场景中达到78%准确率，弥合了弱监督学习与高分辨率物理分割之间的差距。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。