Cross-Difference Semantic Consistency Network for Semantic Change Detection

The objective of Semantic Change Detection (SCD) is to discern intricate changes in land cover while simultaneously identifying their semantic categories. Prior research has shown that using multiple independent branches for the distinct tasks of change localization and semantic recognition is a reliable approach to solving the SCD problem. Nevertheless, conventional SCD architectures rely heavily on a high degree of consistency within the bi-temporal feature space when modeling difference features, inevitably resulting in false positives or missed alerts within change areas. In this paper, we introduce a SCD framework called the Cross-Differential Semantic Consistency (CdSC) network. CdSC is designed to mine deep discrepancies in bi-temporal instance features while preserving their semantic consistency. Specifically, the 3D-Cross-Difference module, incorporating 3D convolutions, explores the interaction of cross-temporal features, revealing inherent differences among various land features. Simultaneously, deep semantic representations are further utilized to enhance the local correlation of difference information, thereby improving the model's discriminative capabilities within change regions. Incorporating principles from contrastive learning, a Semantic Co-Alignment loss is introduced to increase intra-class consistency and inter-class distinctiveness of dual-temporal semantic features, thereby addressing the challenges posed by semantic disparities. Extensive experiments on two SCD datasets demonstrate that CdSC outperforms other state-of-the-art SCD methods significantly in both qualitative and quantitative evaluations. The code and dataset are available at https://github.com/weiAI1996/CdSC.