计算机技术与发展2024，Vol.34Issue(9)：30-37,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0178
改进的U-Net在建筑物变化检测中的应用
Application of Improved U-Net in Building Change Detection
摘要
Abstract
Change detection is an important task in earth observation applications.However,existing deep learning based change detection methods still face problems such as blurred boundaries between changing objects and backgrounds,and missed detection of small changing targets in high-resolution remote sensing image building change detection tasks.A remote sensing image building change detection method based on Gaussian difference pyramid and attention feature transfer is proposed to address these issues.It adopts an encoder decoder structure and uses Gaussian difference pyramid to obtain multi-scale edge feature information of dual temporal remote sensing images during the encoding stage,which integrates edge feature information at different scales to enhance the ability of image edge feature expression.Introducing an attention feature transmission mechanism in the decoding section,effectively integrating high-level semantic information with low-level building details to capture salient information in features,suppress invalid feature information,and improve the detection ability of small change targets.The proposed method is trained and tested on publicly available LEVIR-CD and WHU-CD datasets.The experimental results show that compared with other similar methods,the improved method demonstrates good adaptability in detecting changes in building targets of different scales.While ensuring low computational power consumption,the accuracy,recall,F1,and Kappa values are significantly improved.关键词
建筑物变化检测/高斯差分金字塔/U-Net/注意力机制/特征融合Key words
building change detection/Gaussian difference pyramid/U-Net/attention mechanism/feature fusion分类
信息技术与安全科学引用本文复制引用
李仙华,陈柏林,陈欢,张宏鸣,王美丽,冯志玺..改进的U-Net在建筑物变化检测中的应用[J].计算机技术与发展,2024,34(9):30-37,8.基金项目
国家自然科学基金面上项目(62276205) （62276205）
陕西省林业科学院科技创新计划(SXLK2021-0214) （SXLK2021-0214）
