AERNet: An Attention-Guided Edge Refinement Network and a Dataset for Remote Sensing Building Change Detection

Advancements in Earth observation technology enable the detection of surface changes in intricate urban environments. Building change detection (BCD) plays a crucial role in urban planning and environmental monitoring. However, existing deep learning-based BCD algorithms exhibit limited capability in feature extraction, feature relationship comprehension, sample imbalance mitigation, and accurate boundary identification for changed objects. To address these challenges, we introduce an attention-guided edge refinement network (AERNet) that employs a global context feature aggregation module (GCFAM) to aggregate information from extracted multi-layer context features. Our approach incorporates an attention decoding block (ADB) guided by enhanced coordinate attention (ECA) to capture channel and location associations between features. Furthermore, we utilize an edge refinement module (ERM) to enhance the network's capacity to sense and refine the edges of changed areas. To tackle the issue of class imbalance and augment the algorithm's feature learning ability, we devise a novel self-adaptive weighted binary cross-entropy (SWBCE) loss function, combined with a deep supervision (DS) strategy. Experiments are conducted on two publicly available datasets, GDSCD and LEVIR-CD, as well as our newly developed high-resolution complex urban scene BCD dataset, i.e., HRCUS-CD. The latter dataset comprises 11,388 pairs of images at 0.5-meter resolution and over 12,000 labeled change buildings. Comparative experiments indicate that AERNet surpasses advanced competitive methods, while ablation experiments demonstrate the effectiveness of AERNet's model components and the SWBCE loss function. Efficiency comparison confirms that AERNet achieves comprehensive detection performance with superior effectiveness and robustness.