Dual-Branch Seasonal Error Elimination Change Detection Framework Using Target Image Feature Fusion Generator

In change detection tasks, seasonal variations in spectral characteristics and surface cover can negatively impact performance when comparing image pairs from different seasons. Many existing change detection methods do not specifically address the performance degradation caused by seasonal errors. To tackle this issue, the Dual-Branch Seasonal Error Elimination Change Detection Framework using Target Image Feature Fusion Generator (DBSEE-CDF) is introduced. Specifically, the approach utilizes the Target Image Feature Fusion Generator (TIFFG), which incorporates spatial and channel attention mechanisms to extract features from target images and integrates them with deep features from input images using cross-attention. To avoid a severe loss of visual fidelity caused by the significant differences in texture and color features between snow-covered and snow-free images, as well as the different requirements for style transformation, different generators for snow-covered winter images and snow-free winter images are employed to produce intermediate images that eliminate seasonal errors before conducting change detection tasks. The experimental results demonstrate significant improvements in both quantitative and qualitative assessments of change detection tasks compared to directly performing change detection with various models, highlighting the effectiveness of the proposed DBSEE-CDF.