自然资源遥感2026，Vol.38Issue(3)：55-64,10.DOI:10.6046/zrzyyg.2025176
面向复杂背景无人机影像的水体分割网络
Water body segmentation network for unmanned aerial vehicle images with complex backgrounds
摘要
Abstract
Extracting information on water bodies from unmanned aerial vehicle(UAV)images faces challenges,such as occlusion-induced interference,misclassification caused by mixed pixels,and omission of tiny water bodies.To address these issues,this study proposed a high-precision water body segmentation network that integrates the residual structure with an attention mechanism.First,a deep encoder with ResNet50(i.e.,a residual network with 50 layers)as the architecture was constructed to enhance semantic feature representation through residual connections.Second,a channel-spatial dual-dimensional attention mechanism was introduced into skip connections to achieve dynamic feature calibration.The channel attention reweighed the water body saliency,while the spatial attention focused on areas sensitive to water body boundaries.Third,a hybrid Focal-Dice loss function was designed to reduce boundary overlaps of tiny water bodies through hard sample mining,thereby achieving co-optimization of class imbalance and spatial structural information.The proposed network was compared with four mainstream models,i.e.,fully convolutional network(FCN),SegNet,DeepLabV3+,and classic U-Net.The results from qualitative analysis and quantitative evaluation demonstrate that the proposed network outperformed all the models,yielding a precision of 98.29%and a recall of 97.00%.Therefore,the proposed network can provide a novel solution that balances accuracy,efficiency,and robustness for water body information extraction from UAV remote sensing images.关键词
水体提取/无人机影像/深度学习/语义分割/U-NetKey words
water body extraction/unmanned aerial vehicle(UAV)image/deep learning/semantic segmentation/U-Net分类
信息技术与安全科学引用本文复制引用
汪博雅,曾微波,张乾坤,姚睿涵..面向复杂背景无人机影像的水体分割网络[J].自然资源遥感,2026,38(3):55-64,10.基金项目
安徽省高等学校科研计划项目"大规模点云高保真轻量化与无痕更新全链关键技术研发及应用"(编号:2025AHGXZK31295)资助. （编号:2025AHGXZK31295）
