GUF-Net: Gated Uncertainty-Aware Fusion Network for Robust Building Change Detection in Remote Sensing Images

Building change detection (BCD) in high-resolution remote sensing imagery has achieved considerable accuracy with deep learning, yet most existing models still suffer from performance degradation under noise interference, partly due to inadequate uncertainty handling. To address this, we propose GUF-Net, a gated uncertainty-aware fusion network that integrates three key components: (1) a pixel-wise uncertainty modeling module that generates a pixel-level uncertainty map alongside the change probability map; (2) an adaptive gated fusion mechanism that dynamically weights multi-scale features and uncertainty information; and (3) a redesigned loss function that can directly supervise the uncertainty estimation, ensuring that it reflects prediction reliability rather than serving as an unsupervised auxiliary output. Experiments on three public benchmarks (WHU-CD, CDD, and LEVIR-CD) show that GUF-Net achieves competitive F1 scores of 93.88%, 96.55%, and 90.43%, respectively, ranking top three on CDD and WHU-CD, while its advantage is less pronounced on LEVIR-CD. Under the strongest Gaussian, salt-and-pepper, and speckle noise conditions on the WHU-CD test set, the F1 score of GUF-Net decreases by only 0.86, 3.95, and 1.29 percentage points, respectively, exhibiting the smallest degradation among the compared models. Unlike existing uncertainty-aware baselines evaluated solely on clean data, GUF-Net jointly supervises evidential pixel uncertainty and routes it through adaptive gating, enabling stable fusion under degraded imaging conditions. Ablation studies confirm that the gated fusion, uncertainty injection, and global context aggregation each contribute to the overall performance. These results suggest that explicit uncertainty modeling and adaptive fusion can improve the reliability of BCD under noisy conditions, although there remains room for further improvement in clean-set accuracy and model efficiency.