Semi-supervised Remote Sensing Image Change Detection Based on Mean Teacher Model with Multi-level Perturbations
Currently,deep learning methods have made significant progress in remote sensing image change detection.However,the existing approaches in remote sensing image change detection primarily rely on fully supervised networks,which heavily depend on the quantity and quality of label data.In order to solve these problems,this paper proposes UniMTCD-Net,a semi-supervised remote sensing image change detection network that combines the mean teacher model with multi-level perturbation.Firstly,different types of strong perturbations are separated into different branches for learn-ing and consistency is constrained to form a diversified perturbation space,avoiding the difficulty of single branch learning and effectively improving the utilization efficiency of unlabeled data.Secondly,by using the mean teacher model,not only the difference between the pseudo labels generated by the teacher model and the strong predictions output by the student model are extended,but also the teacher model parameters are updated by exponential moving average(EMA),making the generation of pseudo labels more accurate.Experimental results demonstrate that compared with mainstream semi-supervised methods,UniMTCD-Net has better detection performance,especially under 5％labeled training data,the detection per-formance is more superior,further verifying the effectiveness and superiority of UniMTCD-Net in remote sensing image change detection.
change detectionsemi-supervisedconsistencymean teacher model
国家科技期刊平台
NSTL
万方数据
