Semi Supervised Change Detection Method of Remote Sensing Image

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.