In recent years, significant progress in deep learning and computer vision has promoted the development of remote sensing change detection. However, the existing methods still rely on a single visual modality and cannot effectively utilize information from other modalities, such as structure priors such as elevation maps or depth maps. In order to make full use of structure prior information such as depth maps, this paper propose a novel very-high-resolution remote sensing change detection framework SPP-CD with structure prior perception ability. The framework accepts bi-temporal optical remote sensing images and corresponding depth maps as input, uses a siamese encoder structure to extract features from the input data, and then uses a feature interaction module to interact and fuse the bitemporal features. Finally, a feature decoder is used to decode the fused features and output a fine-grained pixel-level change detection map. A dual path fusion multimodal feature encoder is designed based on the SPP-CD framework, which utilizes a global path based on cross modal attention and a local path based on convolution to enable the model to combine long-distance context modeling, cross modal feature modeling, and fine feature extraction capabilities. Experimental results on the LEVIR-CD dataset show that compared with the existing single modal baseline methods, the method proposed by this paper achieved 92.36%, 85.81% and 99.21% on the key indicators F1, IoU and OA respectively, surpassing the single-modal baselines, thus proving that integrating spatial structure prior information can effectively improve the change detection performance and alleviate the limitations of existing methods that mainly rely on visual information.