Dual-Dimension Feature Interaction for Semantic Change Detection in Remote Sensing Images

Remote sensing semantic change detection (SCD) involves extracting information about changes in land cover/land use (LCLU) within the same area at different times. This issue is of crucial significance in many Earth observation tasks, such as precise urban planning and natural resource management. However, the current methods primarily focus on spatial feature extraction, lacking awareness of temporal features. Consequently, there are challenges in extracting change features, making distinguishing intraclass and interclass differences difficult. This also contributes to pseudochange, posing challenges for SCD tasks. To overcome the limitations of existing methods, we present a dual-dimension feature interaction network (DFINet) for SCD. First, to enhance the assessment and perceptual abilities related to intraclass and interclass differences, we introduce a temporal difference feature enhancement (TDFE) module. This module comprehensively captures features from the temporal dimension. Then, to address the interrelation between multitemporal and multilevel features, we investigate the feature selection interaction (FSIA) and interaction attention modules (IAM), which enable multidimensional deep fusion and interaction of change features. This enhances the capacity for information transfer and integration among the features within multitemporal remote sensing images (RSIs). The experimental results demonstrate that, compared to existing methods, the proposed architecture achieves a significant improvement in accuracy. Additionally, the design enhancements added to DFINet boost the practicality of remote sensing SCD, underscoring its substantial research value.