 The MHC-NET model is designed for detecting changes in remote sensing images. It consists of four modules, cross-scale feature attention module, CSAM, global semantic filtering module, GSFM, double branch information fusion module, BIFM, and similarity enhancement module, SEM. CSAM is used to extract semantic information related to the change in the remote sensing image from the difference branch. GSFM filters the rich semantic information in the remote sensing image. The BIFM fuses the semantic information extracted from the difference branch and the global branch. Finally, SEM uses the similar information extracted with the similar branch to correct the details of the feature map in the feature recovery stage. This article was authored by DHEO Wang, LIGUA Wang, MINCIA, and others.