Semantic Segmentation of Weakly Annotated Remote Sensing Images Based on Feature Adversary and Uncertainty Perception

The rich details in remote sensing images require the expertise of domain experts for annotation, making precise labeling across multiple scenarios challenging. Currently, there is an increasing interest in low-precision image-level annotations. However, errors and omissions are difficult to avoid during the labeling process. How to achieve accurate semantic segmentation under noisy annotations has become an urgent problem that requires resolution. In this letter, we consider the rich features of remote sensing targets and design a weakly labeled semantic segmentation model for remote sensing images based on feature adversary and uncertainty perception. First, we introduce a confidence model voting method to handle missing or incorrect image-level labels. Subsequently, we perform multilevel feature fusion to obtain initial pixel-level pseudo labels. Finally, leveraging the significant differences in image features across different categories, we design a feature adversarial model and introduce an uncertainty analysis method, improving the utilization of remote sensing image features and enhancing the accuracy of semantic segmentation. The effectiveness of this approach is validated through a comprehensive evaluation of four datasets.