Remote Sensing Image Change Detection Algorithm Based on Improved STANet
Remote sensing image change detection is to identify the significant changes between dual temporal images.Given two registration images taken at two different times,changes in lighting and mismatch errors can mask the changes in real objects.Exploring the relationship between different spatiotemporal pixels can improve the performance of remote sensing image change detection methods.In Spatial Temporal Attention Neural Network(STANet),a twin based spatiotemporal attention neural network is proposed,based on which some improvements are made.① By designing an up-sampling module for the distance measurement module,the problem of fuzzy feature gaps caused by linear interpolation is solved,making the gap in the changing area more obvious and the false alarm rate lower.② To address the high computational cost of STANet's PAM(Pyramid Spatial Temporary Attention Module)module,a new Coordinate Attention(CA)attention module is introduced to better identify the features of different spaces and channels while reducing the computational cost.③ To solve the problem of insufficient utilization of the feature maps extracted from Residual Network(ResNet)by STANet,a deep supervision module is added to calculate a weight attenuation loss using the features of the middle layer,which plays a role of regularization.The experiment shows that the improved network improves the Fl score of the baseline model from 81.6 to 86.1.The experimental results on public remote sensing image datasets show that the improved method outperforms several other advanced methods.
万方数据
维普
