High-precision extraction of water bodies in mountainous areas from multi-polarization SAR images via deep learning that integrates multi-scale radar backscatter characteristics

doi:10.13474/j.cnki.11-2246.2026.0201

Abstract

Abstract: To address the challenges of frequent water misclassification and difficulty in preserving water boundary details caused by mountain shadows,dense vegetation,and SAR coherent speckle noise in complex terrains,this study proposes a novel deep learning-based water extraction method for SAR imagery that integrates multi-scale radar backscattering features through multi-polarization SAR data.The developed approach employs multi-resolution residual convolution and multi-scale feature identity mapping to effectively characterize the rich multi-scale terrain features in complex mountainous environments using multi-polarization SAR imagery,thereby enhancing both accuracy and completeness in water body extraction.Experimental validation was conducted using Sentinel-1 dual-polarization data covering a reservoir in Shanxi Province,with comparative analysis against classical SVM,MRF algorithm,and U-Net model through qualitative and quantitative assessments.The experimental results showed that 92.14% F1,91.39% Kappa coefficient,85.43% IoU,and 98.62% OA.This indicates that the method proposed in this paper has the best comprehensive performance in water extraction and can effectively maintain the boundary details of water extraction.

Key words: waterbody extraction, multi-polarization SAR, deep learning, multi-scale, mountain shadows

CLC Number:

P237

YUAN Zhifang, LI Junxiao, KANG Qian. High-precision extraction of water bodies in mountainous areas from multi-polarization SAR images via deep learning that integrates multi-scale radar backscatter characteristics[J]. Bulletin of Surveying and Mapping, 2026, 0(2): 1-6.

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