Refinement and Collaboration of Difference and Semantic Features for Semantic Change Detection

Semantic change detection (SCD) extends the binary change detection (BCD) task, as it not only locates change areas but also identifies change transition types. Recent research has verified that a multitask learning network performs well in tackling the SCD task, which jointly addresses the two subtasks of change localization and semantic identification. However, it remains a challenge to specifically optimize the distinct features of these two subtasks and establish their interaction to further enhance the overall performance of the multitask network efficiently. In this letter, we propose a novel SCD method that emphasizes the refinement and collaboration of difference and semantic features (RCDSFs). Specifically, a difference feature refinement branch (DFRB) is designed to integrate temporal information and highlight the difference features. Simultaneously, a semantic context refinement branch (SCRB) is developed to extract multiscale and cross-scale semantic details. Moreover, a simple yet effective feature interaction-fusion module (FIFM) is incorporated to coordinate the two subtasks, ensuring consistency while providing additional auxiliary information for each other. Comprehensive experiments on two public remote sensing image SCD datasets demonstrate that the proposed method outperforms the state-of-the-art algorithms.