对比使用原始SAR与合成水体指数，发现融合更优
To Remove or Not to Remove Clouds: A Comparative Analysis and Fusion of Raw SAR and Synthetic NDWI for Overcast Water Segmentation
- 用深度学习将SAR转为合成NDWI以降噪增强对比
- 融合原始SAR与合成NDWI使分割精度显著提升
- 适合遥感洪水监测与多源数据融合研究者
洪水期间持续云层遮挡导致光学卫星失效。虽然合成孔径雷达（SAR）可穿透云层，但其原始数据噪声大、对比度低。近期研究利用深度学习将SAR转换为无云合成光学图像用于水体分割等任务。然而，原始SAR既是直接输入也是转换基础，引发关键方法论争议：完全云覆盖下，分割模型应直接使用原始SAR，还是依赖翻译生成的合成归一化差异水体指数（NDWI）？本研究通过实证表明，合成NDWI表现更佳，因其转换过程有效抑制了雷达噪声。由此引出第二个问题：若同时使用两者会如何？基于此发现，我们提出联合框架，将原始SAR与合成NDWI融合至统一模型。该混合方法结合了原始SAR的清晰物理边界与合成NDWI的高对比度，始终优于单一方法。
原文摘要 · Abstract (English)
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.
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