Deep learning-based methods， particularly those employing Siamese network structure， are widely used in remote sensing image change detection. However， extensive experiments reveal that these methods often suffer significant performance degradation when the order of input images is altered. Notably， the ChangeFormer method exhibits a 79.86% drop in the intersection over the union （IoU） metric on the LEVIR-CD dataset， indicating a lack of chronological robustness that severely impacts the practical utility of the change detection model. To address this issue， this article proposes a novel change detection method called chronologic invariant network （CINet）， which integrates chronological alignment module and cross-layer feature mixing module. During the feature extraction phase， a chronological alignment module is introduced. This module employs spatially cross-mix feature maps and chronologic reconstruction to reduce temporal information discrepancies between the two branches at the feature level. Additionally， a cross-layer feature mixing module is designed to blend deep and shallow features using a full-scale connection and difference-guided approach， effectively utilizing feature information from every layer of both branches to improve change detection accuracy under varying input sequences. Experimental results on the LEVIR-CD dataset show that CINet achieves a recall of 90.63% and an IoU of 84.13%， which represent improvements of 1.83 percentage points and 1.65 percentage points over ChangeFormer， respectively. Results from multiple datasets further demonstrate that the proposed method consistently maintains high change detection performance and robust chronological invariance， even when the order of input images is altered， outperforming other methods.