Abstract: Change detection based on remote sensing images holds significant application potential in land source survey updating, and urban development monitoring and planning. Concerning the challenges of change detection based on remote sensing images in practical applications, this study proposed a lightweight change detection method combining the siameseinverted residual structure with self-attention enhancement. Instead of the traditional convolutional neural network structure, the siamese improved inverted residual structure was used as the backbone network to fully extract the feature information and significantly reduce the network complexity. The self-attention enhancement module was employed to improve the network's ability to pay attention to global information. Edge weights were added to the loss function to precisely optimize the details of the extraction results. The multilevel hopping residual connections were applied to fully integrate the global and local features. Finally, the performance of the proposed method was tested on the public and prepared datasets of remote sensing images for change detection, respectively. The results indicate that compared to other change detection methods, the proposed method significantly reduced network parameters and computational complexity while maintaining the detection accuracy, contributing to lightweight models of change detection based on remote sensing images.