Handling Noisy Annotation for Remote Sensing Semantic Segmentation via Boundary-Aware Knowledge Distillation

In recent years, image segmentation has made significant progress, but acquiring annotated data is still a considerable challenge, especially in remote sensing imagery (RSI). The complex structure and inter-category confusion of RSI increase the time-consuming and cost of pixel-level annotation, and noisy annotations inevitably appear. This paper proposes a boundary-aware knowledge distillation method (BAKD) to handle noisy annotations by evaluating their uncertainty. BAKD consists of two core strategies: Predictive Confidence Evaluation (PCE) and Boundary-annotated Reliability Evaluation (BRE). The predictive confidence jointly decided by the teacher and student networks reflects the annotation’s uncertainty. The boundary-annotated reliability directly measures the annotation’s uncertainty based on the distance from the annotation to the semantic boundary. Leveraging these two types of uncertainty information, BAKD assigns each sample a comprehensive boundary-aware weight to identify samples with potential noisy annotations. This alleviates the impact of noisy annotation on the model’s training and improves its generalization performance. Experimental results show that BAKD achieves competitive semantic segmentation performance on the Potsdam and Vaihingen benchmarks compared with the state-of-the-art KD methods. In addition, BAKD can be easily integrated into semantic segmentation methods based on KD, extending their applicability in handling noisy annotations. Codes are available at https://github.com/sunyueue/BAKD.git.