Building change detection (BCD) 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 BCD. 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.

中文翻译：

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用于构建高分辨率遥感图像变化检测的分层渐进识别网络


建筑变化检测（BCD）在城市规划和发展中发挥着至关重要的作用。然而，该领域仍有一些紧迫问题尚未解决，包括复杂背景下建筑物的误检测、分割结果中出现锯齿状边缘以及密集建筑区域中的检测盲点。为了应对这些挑战，本研究创新性地提出了分层自适应渐进识别网络（HAGR-Net）来提高BCD的准确性和鲁棒性。此外，本研究首次采用基于粒子群（ROPS）的强化学习优化算法来优化HAGR-Net的训练过程，从而加速训练过程并减少内存开销。实验结果表明，优化后的 HAGR-Net 在 WHU_CD、Google_CD 和 LEVIR_CD 数据集上优于最先进的方法，实现了 93.13%、85.31% 和 91.72% 的 F1 分数以及并集平均交集 (mIoU) ）得分分别为 91.20%、85.99% 和 90.01%。