现代电子技术2026，Vol.49Issue(17)：46-52,7.DOI:10.16652/j.issn.1004-373X.2026.17.008
基于遥感图像语义分割的海岸线提取与类型判别方法
Method for coastline extraction and type identification based on semantic segmentation of remote sensing image
摘要
Abstract
In view of the complex terrain,significant target variations,and diverse backgrounds in coastal line remote sensing images,this study proposes a U-shaped image semantic segmentation network based on the Swin Transformer(SS-UNet),with the aim of enhancing the accuracy of coastal line extraction and classification.The model achieves performance optimization based on the following advancements:incorporating the Swin Transformer module into the U-Net encoder-decoder architecture to replace traditional pooling and upsampling operations,thereby improving the capability to extract global features;introducing a channel attention mechanism(SENet)within the skip connections to optimize multi-scale feature fusion;designing a loss function that includes a penalty term derived from a distance map to enhance the learning of boundary regions.Experiments demonstrate that in comparison with the benchmark models such as U-Net,UNet++,and DeeplabV3+,the SS-UNet improves mean accuracy by over 1.6%,and can perform automatic differentiation of coastal line types,so it provides an effective solution for large-scale coastal line monitoring.关键词
遥感图像/语义分割/海岸线提取/U-Net/Swin-Transformer/SKNetKey words
remote sensing image/semantic segmentation/coastline extraction/U-Net/Swin-Transformer/SKNet分类
信息技术与安全科学引用本文复制引用
文莉莉,邬满,赖俊翔..基于遥感图像语义分割的海岸线提取与类型判别方法[J].现代电子技术,2026,49(17):46-52,7.基金项目
广西自然科学基金项目:多模态数据驱动下北部湾海洋承灾体易损性智能评价及灾变机理研究(2024JJB170012) （2024JJB170012）
广西科技重大专项:空天地一体协同重大灾害应急智慧服务平台研发与应用示范(桂科AA22068072) （桂科AA22068072）
