## 3 Experiments and Analysis
### Comparisons on Mainstream Methods
#### 3.2.2 Results on LEVIR-CD+ Dataset  
Figure 6 visually compares CD results on images with significant variations in illumination, shadows, and vegetation phenology. RCCD demonstrates robustness to complex backgrounds, achieving higher detection rates (Small-VS) and lower false positive rates (Large-VS). Under consistent radiation (e.g., rows 1 and 2), other methods exhibit missed detections, as feature measurements at the decoding stage cannot effectively distinguish changed buildings from the ground. In scenarios with significant radiation differences (e.g., rows 3-6), other methods often misidentify rooftop illumination and building shadow variations as changes, while RCCD avoids these errors. In the third set of images, the strong contrasts and shadows in the later image due to lighting conditions cause most methods to interpret these dramatic visual differences as changes. Vegetation changes also pose challenges for other methods (e.g., rows 7-10), as dense foliage can obscure buildings, hindering accurate assessment. In the eighth set of images, occlusions from vegetation growth and scene style variations resulting from differences in lighting and shadows lead to a large number of false alarms. RCCD's large-scale convolutions, with their ability to induce bias in local spatial contexts, effectively determine the identity consistency between buildings and their surroundings. Table 3 presents the performance of different methods on the LEVIR-CD+ test set, which exhibits significant variations in illumination, shadows, and phenology. Our RCCD achieved the highest accuracy across all evaluations, with substantial improvement on the Large-VS subset. Compared to CDNeXt, RCCD achieved a 0.54% higher F1-score (3.52% higher than IFNet). On the Large-VS subset, RCCD's F1-score improved by 1.12% (107.4% relative increase), indicating its effectiveness in mitigating interference from radiation and phenological changes. Compared to RCCD-B, our method achieved an additional \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**S2 Looking** & \multicolumn{4}{c}{**Full Dataset**} & \multicolumn{4}{c}{**Large-VS Subset**} & \multicolumn{4}{c}{**Small-VS Subset**} \\ \hline
**Method** & **Pre.** & **Rec.** & **F1** & **IoU** & **Pre.** & **Rec.** & **F1** & **IoU** & **Pre.** & **Rec.** & **F1** & **IoU** \\ \hline UNet & 62.55 & 42.47 & 50.59 & 33.86 & 64.51 & 43.44 & 51.92 & 35.06 & 65.13 & 41.02 & 50.34 & 33.63 \\ FC-Diff & 61.65 & **61.25** & 61.45 & 44.35 & 58.01 & **62.77** & 60.29 & 43.15 & 64.49 & **60.33** & 62.34 & 45.28 \\ FC-Conc & 68.67 & 53.88 & 60.38 & 43.25 & 67.36 & 55.28 & 60.73 & 43.60 & 70.63 & 52.17 & 60.01 & 42.87 \\ IFNet & 66.18 & 48.56 & 56.02 & 38.90 & 61.18 & 50.77 & 55.49 & 38.40 & 68.65 & 45.51 & 54.74 & 37.68 \\ SNUNet & 69.54 & 53.82 & 60.68 & 43.55 & 67.76 & 55.75 & 61.17 & 44.06 & 69.99 & 52.92 & 60.27 & 43.13 \\ L-UNet & **72.46** & 53.26 & 61.39 & 44.29 & 70.51 & 53.75 & 61.00 & 43.88 & **73.33** & 51.95 & 60.82 & 43.70 \\ BIT & 67.74 & 55.95 & 61.28 & 44.18 & 67.05 & 56.44 & 61.29 & 44.18 & 69.31 & 54.63 & 61.10 & 43.99 \\ ICIF-Net & **72.68** & 50.77 & 59.78 & 42.63 & 68.94 & 52.97 & 59.91 & 42.77 & **75.43** & 47.58 & 58.36 & 41.20 \\ P2V & 68.74 & 51.61 & 58.96 & 41.80 & 67.04 & 52.75 & 59.04 & 41.89 & 70.46 & 50.42 & 58.77 & 41.62 \\ CDNeXt-B & 70.89 & 60.29 & 65.16 & 48.33 & 69.45 & 62.16 & 65.61 & 48.82 & **73.81** & 58.53 & **65.29** & **48.47** \\ CDNeXt & 70.78 & **63.08** & **66.71** & **50.05** & **72.15** & **62.21** & **66.81** & **50.16** & 70.08 & **62.36** & **66.00** & **49.25** \\ \hline RCCD-B & **72.96** & 59.91 & **65.79** & **49.02** & **74.11** & 59.80 & **66.19** & **49.47** & 71.97 & 58.87 & 64.76 & 47.89 \\ RCCD & 71.10 & **64.43** & **67.60** & **51.06** & **71.83** & **65.94** & **68.76** & **52.40** & 71.36 & **63.77** & **67.35** & **50.77** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative performance of RCCD and competing methods on the S2 Looking. Color convention: best, 2 nd-best, and **3 rd-best**.