Change detection based on deep learning is an important research direction in intelligent interpretation of remote sensing images. It has developed rapidly in recent years, but it is also a long-term challenge in remote sensing applications. This is mainly because the production of labeled data for training requires a lot of labor costs, and the currently available change detection labeled data is relatively small. While the complexity of high-resolution remote sensing imagery greatly increases the difficulty for deep learning models to learn robust and discriminative representations from scenes and objects, in this case, training deep learning models with a small amount of labeled data is still a huge challenge. To address this issue, this paper proposes a semi-supervised learning change detection method based on Generative Adversarial Networks (GAN). Compared with previous techniques, this paper combines a typical GAN framework with a Siamese network and applies it to change detection in remote sensing images. We introduce residual networks and atrous convolutions into Siamese networks, and employ a flow alignment module (FAM) to learn semantic flow between adjacent hierarchical feature maps. The connected discriminator formulates the training of the generator as a min-max optimization problem. Comprehensive quantitative and qualitative evaluations of multiple models show that our proposed method outperforms state-of-the-art change detection algorithms.