北京测绘2026，Vol.40Issue(3)：269-276,8.DOI:10.19580/j.cnki.1007-3000.2026.03.001
融合SAR影像与辅助数据的图神经网络地表水体提取方法
Method for extracting surface water bodies using combined SAR imagery and auxiliary data with graph neural networks
摘要
Abstract
In the extraction of surface water bodies from remote sensing imagery,synthetic aperture radar(SAR)images are often affected by speckle noise and complex background interference,leading to blurred boundaries and misjudgment.To address this issue,this paper proposed a method that integrated SAR imagery with auxiliary data using graph neural net-works.First,superpixel segmentation was applied to convert the image into a graph structure with superpixels as nodes,which helped to smooth out noise while reducing the number of processing units.Multi-source features related to water body extraction and identification were extracted based on SAR imagery and digital elevation model(DEM),and random forests were utilized for feature selection and dimensionality reduction to enhance feature discriminability.Subsequently,a deeper graph convolutional network(DeeperGCN)was introduced to classify the superpixel nodes,achieving precise water body identification.Experiments on real datasets demonstrate that the proposed method outperforms mainstream baseline models in terms of overall accuracy(0.992 1),precision(0.966 2),recall(0.964 4),and F1 score(0.965 3),showcasing good gen-eralization capabilities and providing a novel approach for the fusion of multi-source remote sensing data and the application of graph neural networks.关键词
地表水体提取/图神经网络/合成孔径雷达/数字高程模型/多源特征Key words
surface water body extraction/graph neural network/synthetic aperture radar/digital elevation model/multi-source features分类
天文与地球科学引用本文复制引用
邹慧敏,朱建华,刘晓建,田震..融合SAR影像与辅助数据的图神经网络地表水体提取方法[J].北京测绘,2026,40(3):269-276,8.基金项目
海南省重点研发项目(ZDYF2023GXJS023) （ZDYF2023GXJS023）
海南省科技专项(G6240QT08). （G6240QT08）
