融合VV/VH极化数据，提升复杂区域洪水测绘精度
Cross-Polarization Fusion of VV AND VH SAR Observations for Improved Flood Mapping
- 用深度学习联合分析VV与VH极化数据的互补信息
- 融合模型在植被区和复杂地物区的交并比提升显著
- 适合灾害监测中需高精度洪水边界的场景
合成孔径雷达（SAR）因其全天候、全天时成像能力，被广泛用于洪水监测。然而，在地表散射与体积散射共存的复杂环境中，仅使用单极化SAR数据进行洪水制图仍具挑战性。本文研究了VV与VH极化SAR观测的交叉极化融合对洪水制图的改进效果。采用基于深度学习的分割框架，联合利用VV与VH极化的互补信息。为确保公平对比，在相同训练条件下比较三种配置：仅用VV、仅用VH、以及融合的VV-VH输入。性能通过交并比（IoU）和F1分数等标准指标评估，并结合定性视觉分析。实验结果表明，VV-VH融合模型在各类区域均优于单极化模型，尤其在植被覆盖和异质性高的洪水区域，显著提升了洪水边界的识别精度。研究结果强调了交叉极化SAR融合对提升灾害监测中SAR洪水制图可靠性的关键作用。
原文摘要 · Abstract (English)
Synthetic Aperture Radar (SAR) imagery is widely used for flood monitoring due to its all-weather and day-night imaging capability. However, flood mapping using single-polarization SAR data remains challenging in complex environments where surface and volume scattering coexist. In this paper, we investigate the effectiveness of cross-polarization fusion of VV and VH SAR observations for improved flood mapping. A deep learning-based segmentation framework is employed to jointly exploit complementary information from VV and VH polarizations. To ensure a fair evaluation, three configurations are compared under identical training conditions: VV only, VH only, and fused VV-VH input. Performance is assessed using standard flood mapping metrics, including Intersection over Union (IoU) and F1-score, along with qualitative visual analysis. Experimental results demonstrate that VV-VH fusion consistently outperforms single-polarization models, particularly in vegetated and heterogeneous flood regions, leading to more accurate flood boundary delineation. The findings highlight the importance of cross-polarization SAR fusion for enhancing the reliability of SAR-based flood mapping in disaster monitoring applications.
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