Cross-Polarization Fusion of VV AND VH SAR Observations for Improved Flood Mapping

TL;DR

This paper demonstrates that fusing VV and VH SAR observations using deep learning significantly improves flood mapping accuracy, especially in complex, vegetated environments.

Contribution

It introduces a cross-polarization fusion approach with a deep learning segmentation framework, outperforming single-polarization methods in flood delineation tasks.

Findings

VV-VH fusion outperforms single-polarization models in accuracy.

Fusion improves flood boundary delineation in vegetated areas.

Deep learning-based segmentation effectively exploits polarization complementarity.

Abstract

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.