To address the issues of original image feature loss and unexpected noise introduction in optical and synthetic aperture radar (SAR) remote sensing image change detection as well as to improve the quality and accuracy of remote sensing image change detection, a domain adaptive neural-network-based optical and SAR remote sensing image change detection method is proposed. Domain adaptive constraints were first introduced to align the extracted heterogeneous depth features to a common depth feature space, thereby improving the performance of heterogeneous image change detection. A final change map was then generated by inputting aligned depth features into the multi-scale decoder. Experiments were conducted to assess the effectiveness of the proposed method, wherein three typical datasets and six advanced detection methods were selected for comparative analysis. Experimental results show that the average accuracy, recall, segmentation performance, and weighted value performance of the proposed detection method on the three datasets are 80.81%, 84.39%, 73.67%, and 82.58%, respectively, which are better than those of the comparison methods.