测试科学与仪器2025，Vol.16Issue(3)：371-383,13.DOI:10.62756/jmsi.1674-8042.2025036
基于FPCNet的遥感图像变化检测
FPCNet-based change detection for remote sensing images
李积英 1王奇 1石红萍1
作者信息
- 1. 兰州交通大学 电子与信息工程学院,甘肃 兰州 730000
- 折叠
摘要
Abstract
The objective of this study is to address semantic misalignment and insufficient accuracy in edge detail and discrimination detection,which are common issues in deep learning-based change detection methods relying on encoding and decoding frameworks.In response to this,we propose a model called FlowDual-PixelClsObjectMec(FPCNet),which innovatively incorporates dual flow alignment technology in the decoding stage to rectify semantic discrepancies through streamlined feature correction fusion.Furthermore,the model employs an object-level similarity measurement coupled with pixel-level classification in the PixelClsObjectMec(PCOM)module during the final discrimination stage,significantly enhancing edge detail detection and overall accuracy.Experimental evaluations on the change detection dataset(CDD)and building CDD demonstrate superior performance,with F1 scores of 95.1%and 92.8%,respectively.Our findings indicate that the FPCNet outperforms the existing algorithms in stability,robustness,and other key metrics.关键词
遥感图像变化检测/语义不对齐/双流对齐/深度监督判别Key words
remote sensing image change detection/semantic misalignment/dual flow alignment/deep supervised discrimination引用本文复制引用
李积英,王奇,石红萍..基于FPCNet的遥感图像变化检测[J].测试科学与仪器,2025,16(3):371-383,13.
