Uncertainty-Aware Noisy Label Learning for Remote Sensing Change Detection

Deep learning (DL)-based methods have achieved tremendous success in remote sensing (RS) change detection (CD). However, most DL-based methods heavily rely on high-quality labeled samples, where noisy labels are inevitably introduced during the annotation process, especially at edge regions. Under the supervision of such noisy labels, the performance of RS CD models will deteriorate significantly. To break the limitation, a novel uncertainty-aware noisy label learning network (UNLLNet) is proposed for RS CD, termed UNLLNet. Specifically, a joint detection strategy based on uncertainty analysis is proposed, which leverages samples characterized by low uncertainty and high probability to detect and correct potential noisy labels, thereby mitigating the risk of erroneous correction. Given that noisy labels are more likely to be introduced at edge regions, an edge-guided interlevel difference refinement module (EIDRM) is designed to effectively calibrate the edge structure of change regions, thereby facilitating better identification of noisy labels at edge regions. Moreover, a noise-robust loss function with adaptive hard sample enhancement is introduced, assigning loss weights to each sample based on their uncertainty to further enhance the model’s robustness over noisy labels. Experimental results on LEVIR-CD, CDD, and CLCD datasets validate the effectiveness and advantages of UNLLNet compared to other state-of-the-art RS CD methods.