无线电工程2025，Vol.55Issue(6)：1256-1264,9.DOI:10.3969/j.issn.1003-3106.2025.06.013
基于语义分割模型与遥感影像波段扩展的水边线提取
Waterline Extraction Based on Semantic Segmentation Models and the Extension of Remote Sensing Data Bands
摘要
Abstract
In this study,the performance of three widely-used semantic segmentation models(U-Net,PSP-Net,and DeepLabV3+)is systematically evaluated for waterline extraction tasks using both three-band(RGB)and four-band(RGB+near-infrared band)remote sensing imagery.The effects of different model architectures and spectral band combinations on extraction accuracy are compared through Mean Pixel Accuracy(MPA),Intersection over Union(IoU),and mean IoU(MIoU)metrics.Experimental results demonstrate that DeepLabV3+achieves optimal extraction performance with an MPA of 90.32%.The incorporation of near-infrared bands significantly enhances model accuracy,improving mIoU by 3.92%for U-Net and 3.0%for DeepLabV3+respectively.This spectral augmentation effectively mitigates under-extraction and mis-extraction phenomena during water body identification processes.关键词
遥感影像/语义分割/水边线提取/近红外波段/深度学习Key words
remote sensing imagery/semantic segmentation/waterline extraction/near-infrared band/deep learning分类
海洋科学引用本文复制引用
田森,蔺楠..基于语义分割模型与遥感影像波段扩展的水边线提取[J].无线电工程,2025,55(6):1256-1264,9.基金项目
陕西省秦创原"科学家+工程师"队伍建设项目(2024QCY-KXJ-093) Qinchuangyuan'Scientists+Engineers'Team Con-struction Program of Shaanxi Province(2024QCY-KXJ-093) （2024QCY-KXJ-093）
