北京测绘2026，Vol.40Issue(2)：150-156,7.DOI:10.19580/j.cnki.1007-3000.2026.02.003
基于深度学习的遥感影像建筑物变化检测方法
Deep learning-based building change detection method for remote sensing images
李伟林 1刘宇 2刘文静 3高欣圆2
作者信息
- 1. 北京国测星绘信息技术有限公司,北京 100040
- 2. 自然资源部国土卫星遥感应用中心,北京 100048
- 3. 北京星慧科技有限公司,北京 102200
- 折叠
摘要
Abstract
In the research on building change detection in remote sensing images,issues such as interference from irrelevant targets and insufficient feature interaction between bi-temporal data have long constrained the improvement of detection accu-racy.To address these technical challenges,this paper proposed a deep learning-based building change detection method for remote sensing images.The core of this method lies in the construction of a multi-scale feature fusion mechanism,which consists of three key components:a feature extraction module for multi-level feature acquisition,a computation module for feature difference analysis,and an upsampling module for feature reconstruction.Firstly,a feature extraction scheme was designed that balanced computational efficiency and model simplicity,optimizing the use of computational resources while maintaining feature representation capabilities.Secondly,a spatiotemporal feature collaboration mechanism was formed by integrating temporal feature interaction with spatial feature aggregation,which not only reduced the impact of irrelevant tar-gets but also promoted deep integration of bi-temporal features.Finally,a progressive multi-scale difference feature mask upsampling technique was developed to enhance the reconstruction quality of changing regions.To evaluate the method's performance,experiments were conducted on two publicly available benchmark datasets,large-scale earth vision for image change recognition building change detection dataset(LEVIR-CD)and Wuhan University change detection dataset(WHU-CD).The results show that the proposed method achieves an F1 score of 92.15%on the LEVIR-CD dataset and 90.47%on the WHU-CD dataset,outperforming existing mainstream methods and strongly demonstrating the value and practical appli-cation prospects of this approach.关键词
遥感影像/建筑物变化检测/多尺度特征融合/差异特征掩膜Key words
remote sensing image/building change detection/multi-scale feature fusion/difference feature mask分类
天文与地球科学引用本文复制引用
李伟林,刘宇,刘文静,高欣圆..基于深度学习的遥感影像建筑物变化检测方法[J].北京测绘,2026,40(2):150-156,7.
