To address the issue of false changes in remote-sensing image-change detection caused by lighting conditions, seasonal variations, and differences in objects, this paper proposes a change-detection method based on depth-information fusion. First, depth information from remote-sensing images was extracted via a depth-estimation network as auxiliary information. Second, a self-supervised learning module based on aligned representation and mask modeling was designed to extract texture features with global semantic separability and higher-order representations of depth information. Finally, selective feature fusion and edge-enhancement mechanisms were employed to effectively suppress noise introduced during depth-map generation, thus resulting in fully integrated texture and higher-order features. This method yields F1 scores of 90.35% and 92.60% as well as intersection-over-union (IoU) scores of 82.40% and 86.22% on the LEVIR-CD and CDD datasets, respectively. Experimental results demonstrate the effectiveness of this method in suppressing pseudo-changes.