## 4 Discussion  
The proposed RCCD framework demonstrates significant advancements in handling cross-visual style challenges for remote sensing CD. Our experimental results reveal three critical insights: First, enforcing representation consistency at both encoding and decoding stages proves more effective than single-stage constraints (Table 7), particularly for datasets combining global style variations and local content discrepancies. This hierarchical consistency learning aligns with recent findings in domain adaptation, suggesting that multi-level feature alignment better captures cross-domain relationships. Second, the dataset-specific optimal kernel sizes for LS modules (15 for S2 Looking vs. 11 for LEVIR-CD+) correlate with spatial resolution differences (0.65 m vs. 0.5 m), establishing a practical guideline for kernel size selection as 23\(\times\) ground resolution. Third, our visual style validation method  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **S2 Looking F1** & **LEVIR-CD+ F1** & **Params (M)** & **FLOPs (G)** \\ \hline UNet & 50.59 & 74.22 & 1.35 & 3.58 \\ FC-Diff & **61.45** & 75.80 & 1.35 & 4.73 \\ FC-Conc & 60.38 & 74.21 & 1.54 & 5.33 \\ IFNet & 56.02 & **84.16** & 35.73 & 82.26 \\ SNUNet & 60.68 & 82.67 & 10.20 & 44.38 \\ L-UNet & 61.39 & 81.51 & 8.45 & 17.33 \\ BIT & 61.28 & 83.84 & 3.04 & 8.75 \\ ICIF-Net & 59.78 & 83.32 & 23.84 & 25.41 \\ P2V & 58.96 & 79.45 & 5.42 & 32.96 \\ CDNeXt & 66.71 & 87.14 & 39.42 & 15.76 \\ \hline RCCD-training & - & - & 50.13 & 17.72 \\ RCCD-valid & **67.60** & 87.68 & 37.84 & 15.18 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Computational complexity analysis across different methodologies, encompassing Parameters (Params) count, FLOating-Point OPERations (FLOPs), and F1-score comparisons on S2 Looking and LEVIR-CD+ datasets. Lower values of Params and FLOPs indicate superior efficiency. RCCD-training denotes computational complexity during inference in the training phase, while RCCD-valid represents RCCD complexity during validation procedures. Color convention: best, 2 nd-best, and **3 rd-best**.  
provides a novel quantitative framework for CD algorithm evaluation, addressing a critical gap in current benchmarking practices.  
The effectiveness of RCCD stems from its dual mechanism: (1) The GS module acts as a global style normalizer through Gram matrix alignment, reducing domain shifts caused by seasonal/illumination variations. (2) The LS module functions as a local context stabilizer, mitigating false alarms from transient environmental changes through large receptive field analysis. This dual approach outperforms existing single-strategy solutions like metric learning or attention mechanisms, particularly in complex scenarios combining both global and local variations. However, two limitations warrant discussion: (1) The current LS module focuses on rectangular context regions, potentially missing irregular-shaped contexts. Future work could explore deformable convolutions for adaptive context shaping. (2) While our style validation method effectively identifies challenging samples, it relies on pre-trained features that may not optimally represent CD-specific characteristics. Developing task-oriented style metrics could further enhance validation accuracy.  
Practically, RCCD's robustness enables reliable CD in applications requiring cross-seasonal analysis, such as post-disaster assessment and illegal construction monitoring. The framework's modular design permits integration with various backbone architectures, suggesting broad applicability beyond building CD. Future research directions include extending RCCD to multi-temporal CD scenarios and investigating self-supervised adaptation for unseen style variations.