High-Performance Under Sparse Annotations: Cross-Prototype Bidirectional Constrained Semi-Supervised Framework for Remote Sensing Image Semantic Segmentation

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Abstract
Class imbalance has long posed a significant challenge to semantic segmentation in remote sensing imagery. In semi-supervised settings, existing studies attempt to alleviate this issue through data augmentation, loss function refinement, and optimized training strategies. However, these methods generally rely on an idealized assumption that labeled and unlabeled data share the same class distribution. In practice, unlabeled data typically far exceeds labeled data in scale, making this assumption difficult to satisfy. Such distributional inconsistency not only weakens the effectiveness of semi-supervised learning but also amplifies the inherent class imbalance in remote sensing segmentation. To address these key challenges, we propose a Cross-Prototype Bidirectional Constraint framework (CPBCF) for semi-supervised semantic segmentation. The framework aligns unlabeled prototypes with labeled feature representations to enforce class consistency among unlabeled prototypes, preventing them from being overwhelmed by majority-class features in the early training stages and reducing pseudo-label bias. In addition, by imposing class-level constraints between unlabeled features and labeled prototypes, CPBCF enables the learning of minority-class features to be reinforced by information from unlabeled prototypes, thereby improving the model’s ability to recognize rare categories. To further suppress majority-class dominance, a dynamic class-weight adjustment mechanism is introduced, which selectively applies prototype constraints to minority classes. Through this prototype-level bidirectional balancing strategy, CPBCF enhances the model’s capacity to learn robust minority-class representations under the guidance of unlabeled data, effectively mitigating performance degradation caused by class imbalance in semi-supervised scenarios. Extensive experiments on the ISPRS Potsdam and Vaihingen datasets show that our method consistently surpasses state-of-the-art approaches, achieving notable improvements across all benchmarks.
Funding Information
  • Natural Science Foundation of Hunan Province (2026JJ60031)

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