Abstract
Building change detection (BAD) plays a crucial role in urban planning and development. However, several pressing issues remain unresolved in this field, including false detections of buildings in complex backgrounds, the occurrence of jagged edges in segmentation results, and detection blind spots in densely built-up areas. To address these challenges, this study innovatively proposes a Hierarchical Adaptive Gradual Recognition Network (HAGR-Net) to improve the accuracy and robustness of BAD. Additionally, this research is the first to employ the Reinforcement Learning Optimization Algorithm Based on Particle Swarm (ROPS) to optimize the training process of HAGR-Net, thereby accelerating the training process and reducing memory overhead. Experimental results indicate that the optimized HAGR-Net outperforms state-of-the-art methods on the WHU_CD, Google_CD, and LEVIR_CD data sets, achieving F1 scores of 93.13%, 85.31%, and 91.72%, and mean intersection over union (mIoU) scores of 91.20%, 85.99%, and 90.01%, respectively.