Remote sensing image change detection method based on hybrid trunk and high and low-frequency attention

Remote sensing image change detection technology is rapidly advancing under the impetus of deep learning. In this study, a remote sensing image change detection method based on hybrid backbone and high and low frequency attention modules is proposed for the problems of insufficient extraction of feature components of traditional convolutional neural networks in this field of remote sensing image change detection. The method adopts a hybrid trunk network, and through the attention feature fusion module, the features of the two branches of convolutional neural network and Transformer are fused, which can take care of the extraction of local features and global information. Further, this study integrates high and low frequency attention modules to refine the high frequency details and low frequency background information in the image respectively. The implementation of this method significantly improves the quality and depth of feature extraction. Ultimately, the ability to discriminate and extract the location information and features of interest is strengthened by the coordinate attention module, which improves the recognition accuracy of local details. After extensive experimental testing and validation, it is confirmed that the proposed model achieves a significant improvement in performance compared to existing change detection models.