<p>The technology of optical and Synthetic Aperture Radar (SAR) remote sensing images change detection (CD) has vast potential applications in natural resources monitoring and disaster investigation. However, due to their inconsistent imaging mechanisms, challenges such as poor extraction of local features and inaccurate identification of change regions arise. A selective kernel convolution with a global attention mechanism for multitask CD in optical and SAR images is proposed. A novel SKGAM module has been designed, which integrates selective kernel (SK) convolution and global attention mechanism (GAM). This module allows the network to flexibly and efficiently capture key local details and global features when processing complex data, thereby enhancing its representational ability. The triple attention mechanism (TAM) is introduced to capture interactions between channels and spatial dimensions, enhance the feature representation of the change regions, and suppress the feature representation of the unchanged regions. Furthermore, an end-to-end multitask network architecture combining image translation and CD is constructed to address problems such as high data demand, low training efficiency, and intermediate intervention, with facilitation provided by the effective coordination mechanism between the dual tasks. Experiments are conducted on four heterogeneous remote sensing datasets, with M-UNet, DTCDN, TDSCCNet, and MTCDN selected for comparison. The Overall Accuracy (OA) of the proposed method is 99.26%, 96.63%, 95.45%, and 96.26%, respectively, for the four datasets. Compared with other methods, the experimental results demonstrate the effectiveness and feasibility of the proposed method.</p>