<p>Remote sensing image change detection has advanced significantly with the development of convolutional neural networks (CNNs), yet challenges persist in identifying unbalanced changes in foreground–background categories, especially under limited samples and interference from seasonal variations, lighting changes, or structural renovations. The proposed IDJANet architecture provides an efficient solution for change detection (CD) in remote sensing images by enhancing early feature interactions between bi-temporal images. It utilizes a Siamese pre-trained FastSAM Adaptor for multi-level feature extraction, followed by the Iterative Deformable Joint Attention (IDJ-Attention) module, which applies mutually guided attention to suppress irrelevant noise and highlight actual changes. The coupled bi-temporal features are transformed into difference maps using subtraction and fusion operations, effectively capturing significant variations. Finally, deformable convolution replaces traditional convolution in the prediction stage, enabling the model to better adapt to complex geometries and improving its accuracy in detecting local changes. Experimental results on four CD datasets demonstrate the model’s effectiveness, particularly in low-sample scenarios, achieving superior performance while maintaining a favorable balance between accuracy and computational cost. This research highlights a reliable and efficient architecture for addressing the complexities of change detection in remote sensing, offering a robust solution for practical applications.</p>