A deep learning approach for flood inundation mapping in polarimetric SAR images using DCNv3 and vision transformer
ZHANG Chunfang
LIU Peng
WANG Ruili
PAN Deng
MENG Wenmin
YU Haiyang
Abstract：[Objective]Accurate flood inundation detection using Synthetic Aperture Radar(SAR)images remains challenging due to limitations in existing models and the lack of high-quality annotated datasets.This study aims to address these issues by developing a dedicated flood inundation detection dataset based on polarimetric SAR data and proposing a novel deep learning model,FWSARNet,that integrates Deformable Convolutional Networks v3(DCNv3)and Vision Transformer(ViT)to improve detection accuracy and robustness.[Method]A polarimetric SAR-based dataset was constructed using Sentinel-1 imagery,with extensive data augmentation to enhance model generalization.An efficient feature extraction module was designed by combining DCNv3's spatial adaptability with ViT's global feature modeling.This module served as the backbone of the FWSARNet model,which was then trained and validated on two custom-built datasets:Henan720 and Hebei727.[Result]The proposed FWSARNet model outperformed existing deep learning models in delineating complex flood features,including water body edges,small patches,and narrow linear segments.It achieved mean Intersection over Union(mIoU)values of 88.53％on Henan720 and 92.50％on Hebei727,indicating superior performance in diverse flood scenarios.[Conclusion]FWSARNet demonstrates high accuracy and adaptability in flood inundation detection from SAR images and is well-suited for emergency disaster response applications using polarimetric SAR data.
Keywords：flood extent detectionpolarimetric SARdeformable convolutionvision transformer
Publication Date：2025-09-30
Online Publishing Date：2026-09-12(First online date of this platform, not the publication date of the document)
Pages：10( 133-142 )