Change detection (CD) methods for remote sensing images based on deep learning have garnered increasing research attention. However, existing deep learning approaches are often tailored for specific types of sensors. Extending these methods to dual-sensor scenarios presents challenges, including difficulties in data fusion and an increase in parameter numbers. To address these challenges, we propose a novel dual-stream encoder–decoder CD network architecture. In the encoder, the architecture comprises a shared-weight Siamese Unet stream for each sensor, with unique weights for different sensors. Before the decoder, a 3-D attention module (3-D AM) is incorporated, processing encoder outputs and fusing features from different streams. In addition, to mitigate the increased model parameter numbers due to the use of dual sensors, we propose a lightweight Unet architecture along with a time-difference structure in each stream. The proposed model is evaluated across multiple scenarios on a dual-sensor CD dataset, yielding an F1 score of 0.572 and the parameter number of 0.91 M. These results showcase high performance on a cost-effective level. Our code is available at https://github.com/CodeofHuang/SmaDS_SiamUnet.