Siamese Generative Adversarial Network for Change Detection Under Different Scales

Change detection methods based on low-resolution (LR) images with higher temporal resolution often lead to fuzzy results, while high-resolution images (HRIs) can provide more detailed information to solve this problem. However, it's hard to obtain two tiles of HRIs with high-quality for rapid change detection in actual production due to low temporal resolution and high cost. Therefore, it is necessary to explore a change detection method combing low- and high-resolution images to acquire urban change areas more accurately and quickly. In this paper, an end-to-end siamese generative adversarial network (SiamGAN) integrating a super resolution network and the siamese structure was proposed for change detection under different scales. The super-resolution network is used to reconstruct low-resolution images into high-resolution images, while the siamese structure is adopted as the classification network to detect changes. In the experiments, SiamGAN achieved an F1 of 76.06% and an IoU of 61.52% in the test set, which is respectively 5.68% and 6.92% higher than the CNN-based methods using LR images after bicubic interpolation. The results show that our proposed method can effectively overcome difference in scale between low- and high-resolution images and perform change detection more precisely and rapidly.