Application of Improved U-Net in Building Change Detection
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.
building change detectionGaussian difference pyramidU-Netattention mechanismfeature fusion
国家科技期刊平台
NSTL
万方数据
