Multi-Temporal Images Generation for Building Change Detection Performance Promotion

The changes in building are important basis for urban monitoring. However, due to the rarity and sparsity of the occurrence of changes in buildings, collecting effective bitemporal image pairs is challenging, as it requires long-term observation over several months or even years. Additionally, annotating large-scale change detection datasets is time-consuming and labor-intensive. Consequently, data scarcity issues lead to insufficient training of building change detection models. To address this, we propose a data generation method Building Generation GAN (BG-GAN). Different from other GANs, the BG-GAN is trained based on adversarial consistency loss, enabling the model to generate new bi-temporal image pairs with various types of building changes. To verify the effectiveness of the proposed methods on change detection task, BG-GAN is utilized to perform building change samples generation on two building change detection datasets (LEVIR-CD and WHU-CD). The experimental results demonstrate that the proposed method can improve the robustness and generalization of change detection model to detect pseudo changes.