Change detection in remote sensing imagery plays a vital role in urban planning, resource monitoring, and disaster assessment. However, current methods, including CNN-based approaches and Transformer-based detectors, still suffer from false change interference, irregular regional variations, and the loss of fine-grained details. To address these issues, this paper proposes a novel building change detection network named Dense Cross-Fusion and Spatial Compensation Mamba (DCSC Mamba). The network adopts a Siamese encoder–decoder architecture, where dense cross-scale fusion is employed to achieve multi-granularity integration of cross-modal features, thereby enhancing the overall representation of multi-scale information. Furthermore, a spatial compensation module is introduced to effectively capture both local details and global contextual dependencies, improving the recognition of complex change patterns. By integrating dense cross-fusion with spatial compensation, the proposed network exhibits a stronger capability in extracting complex change features. Experimental results on the LEVIR-CD and SYSU-CD datasets demonstrate that DCSC Mamba achieves superior performance in detail preservation and robustness against interference. Specifically, it achieves F1 scores of 90.29% and 79.62%, and IoU scores of 82.30% and 66.13% on the two datasets, respectively, validating the effectiveness and robustness of the proposed method in challenging change detection scenarios.