Confusion-Aware Contrastive-Based Semi-Supervised Semantic Change Detection Through Space Collaboration

Semantic change detection (SCD) involves detecting changed regions and classifying their corresponding semantic change categories in remote sensing images. However, in most SCD scenarios, only the land-cover category information of the changed regions is provided. The mainstream multi-task Siamese network optimization process fails to fully leverage the information from unchanged land-cover types, which limits its ability to recognize land-cover change types and ultimately limits SCD performance. To address the issue of suppressed change type recognition caused by sparse labels in the SCD task, this paper proposes a confusion-aware contrastive-based semi-supervised semantic change detection method through space collaboration (SC²A-SCD). The proposed SC²A-SCD framework first integrates bi-temporal land-cover classification (LCC)-predicted logits from both the classification and representation spaces using joint classification and representation space modeling (JSM), providing high-quality pseudo-labels for unchanged land-cover types and enhancing the model’s ability to recognize bi-temporal land-cover types. Meanwhile, confusion-aware contrastive learning (CACL) is conducted in the representation space, utilizing confusion sampling strategies to sample more confusable anchors and negatives that are prone to misclassification, thereby effectively improving the representation capability of bi-temporal land-cover types and enhancing the quality of the pseudo-labels obtained via JSM, ultimately boosting the ability of SC²A-SCD to recognize semantic changes. Compared to the state-of-the-art SCD methods, the comprehensive experimental results confirm that the proposed SC²A-SCD framework can effectively improve the recognition ability for change types, demonstrating its effectiveness in enhancing SCD performance.