去还是不去除云：针对阴天水体分割的原始合成孔径雷达（SAR）与合成归一化差异水体指数（NDWI）的对比分析与融合

中文摘要 AI 辅助

洪水期间，持续的云层会使光学卫星失效。合成孔径雷达（SAR）可穿透云层，但其原始数据存在噪声且缺乏清晰的对比度。为缓解该问题，近期研究利用深度学习模型将SAR转换为无云的合成光学图像，用于水体分割等下游任务。然而，由于原始SAR是上述两种操作的原始数据源，产生了关键的方法学困境：在完全阴天条件下，分割模型应直接处理原始SAR，还是依赖转换得到的合成归一化差异水体指数（NDWI）代理？本研究通过实验证明合成NDWI能取得更好的结果，因为转换过程可作为强大的雷达噪声过滤器，解决了该争议。由此引出第二个自然问题：若同时利用两者会如何？基于研究发现，本研究提出一种组合框架，将原始SAR与合成NDWI整合到统一模型中。通过融合原始SAR的清晰物理边界与合成NDWI的高对比度，该混合方法在所有单独方法中始终表现更优。

英文摘要

Persistent clouds blind optical satellites during floods. While Synthetic Aperture Radar (SAR) penetrates clouds, its raw data is noisy and lacks clear contrast. To mitigate this, recent studies utilize deep learning models to translate SAR into cloud-free synthetic optical imagery for downstream tasks like water body segmentation. However, because raw SAR is the original source for both of these operations, a critical methodological dilemma arises: during complete overcast should segmentation models process the raw SAR directly, or rely on a translated synthetic Normalized Difference Water Index (NDWI) proxy? This study resolves the debate by demonstrating that synthetic NDWI yields better results, as the translation process acts as a powerful filter against radar noise. This raises a natural second question: what if we utilize both? Building on our findings, we introduce a Combined Framework that integrates both raw SAR and synthetic NDWI into a unified model. By fusing the sharp physical boundaries of raw SAR with the high contrast of synthetic NDWI, this hybrid approach consistently outperforms all standalone methods.

发表机构

• Bangladesh University of Engineering and Technology（孟加拉工程技术大学）
• Institute of Water and Flood Management（水与洪水管理研究所）

机构由 AI 辅助整理，请以论文原文为准。