More Unlabeled Data Does Matter: A Full-Cycle Framework for Semi-Supervised Semantic Segmentation of Remote Sensing Images

Semi-supervised semantic segmentation has gained considerable attention due to its ability to leverage large amounts of unlabeled data to enhance model generalization. Although increasing the amount of unlabeled data ideally enhances performance, reality contradicts this notion. Research indicates that the reasons for performance degradation span the entire semi-supervised learning process. These include the inconsistent distribution of unlabeled and labeled data at the data level, the susceptibility to interference from unknown categories at the model level, and the semantic drift problem caused by the training strategy. This article refers to these challenges as the “full-cycle” challenge. To address these issues, we propose a full-cycle framework. At the data level, we develop a consistency-based method, beginning with unlabeled data screening, to prioritize unlabeled data that closely aligns with the labeled data distribution in training. At the model level, we introduce a consistency-based semi-supervised semantic segmentation model capable of identifying unknown category regions and distancing their features from known category features, reducing the interference of unknown classes. At the training strategy level, we adopt a progressive approach that gradually incorporates data from simpler to more complex cases. This strategy improves the model’s adaptability to potential data changes, ensures stability, and improves overall generalization performance. Extensive experiments demonstrate that our method significantly outperforms existing state-of-the-art approaches, validating its effectiveness in addressing the full-cycle challenge.