A Bias Correction Semi-Supervised Semantic Segmentation Framework for Remote Sensing Images

The clustering assumption is widely adopted in semi-supervised semantic segmentation methods. However, this assumption heavily relies on high-quality feature representation, leading to learning and cognitive biases if it does not hold. Learning bias entails the potential overfitting of labeled data, leading to capturing local features and consequently misclassifying semantic categories. Cognitive bias indicates the network’s susceptibility to interference features. Recently, the consistency-based mechanism has been proposed to address these biases. By subjecting unlabeled data to diverse weak perturbations, it breaks original clustering features, compelling the model to learn more robust and generalized representations. However, when processing complex remote-sensing images, these weak perturbations often prove ineffective in the later stages of training. To address this, we propose a bias correction framework (BCF). The BCF begins with a feature consistency enhancement module that guides the student model to learn feature representations with greater generalization capability. In the feature decoding stage, we introduce a multi-decoder structure with weakly orthogonal weights to maximize feature differences, thereby further reducing learning and cognitive biases. Additionally, to improve the confidence of pseudo-labels and enhance consistency learning, we design a multi-decoder teacher model based on symmetrical knowledge transfer, allowing the diverse and multi-angle information learned by the student model to be transferred to the teacher model. Extensive experimental results show that our method significantly outperforms state-of-the-art methods on the ISPRS Potsdam and Vaihingen datasets.