水利信息化Issue(1)：43-52,10.DOI:10.19364/j.1674-9405.2026.01.007
语义变化检测技术在河湖岸线监测中的应用研究
Research on application of semantic change detection technology in river and lake shoreline monitoring
摘要
Abstract
To support the implementation of the"Clearing Up the Four Disorders"policy and address low efficiency,long cycles,high costs,and limited accuracy in river and lake shoreline monitoring,a comprehensive semantic label change detection dataset,RLShorelineSCD,was constructed.A convolutional attention module was integrated into the Semantic Change Detection(SCD)task to improve a Bi-temporal Semantic Reasoning Network(Bi-SRNet),leading to the proposed Bi-temporal Convolutional Block Attention Module Network(Bi-CBAMNet).The key shoreline section of the Yangtze River in Yangzhou was selected as the study area.Based on historical monitoring data from 2022 to 2023,the improved network was applied to the prediction dataset.Compared with Bi-SRNet,Bi-CBAMNet improved overall accuracy and F1 scores for segmentation and change detection by 3.97%and 1.52%on the RLShorelineSCD dataset,while achieving changes of 0.25%and-0.09%on the SECOND dataset.These results indicated that the network enhances semantic feature extraction across branches and facilitates the fusion of dual-temporal semantic features.The findings demonstrate significant practical value and broad application prospects for river and lake shoreline monitoring.关键词
语义变化检测/二分类变化检测/注意力模块/遥感监测/河湖岸线/河湖库"清四乱"Key words
semantic change detection/binary change detection/attention module/remote sensing monitoring/river and lake shoreline/"clearing up the four disorders"in rivers and lakes分类
信息技术与安全科学引用本文复制引用
童杨辉,朱敏,王润天,卢向伟,周鑫鑫..语义变化检测技术在河湖岸线监测中的应用研究[J].水利信息化,2026,(1):43-52,10.基金项目
江苏省高等学校基础科学(自然科学)研究面上项目(22KJB420004) （自然科学）
2024年度青海省"昆仑英才·高端创新创业人才"项目(QHKLYC-GDCXCY-2024-434) （QHKLYC-GDCXCY-2024-434）
