We propose a systematic stepwise optimization pipeline for building change detection in dense urban environments using high-resolution CAS500-1 satellite imagery. To support robust model development, we constructed a dataset comprising 3,816 bi-temporal patch pairs across 28 urban regions. The framework employs a Mamba-based architecture as the baseline, leveraging its efficient global context modeling capability for binary change detection. The pipeline integrates three sequential optimization stages to enhance detection accuracy and stability. First, we evaluated normalization techniques tailored for 12-bit radiometric resolution, comparing percentile-based scaling, gamma correction, and log transformations. Second, we implemented an augmentation strategy that extends standard geometric transformations with optical and temporal methods to improve generalization in structurally complex urban settings. Third, we explored various ensemble configurations, including confidence-weighted and hierarchical aggregation to mitigate individual model scale limitations. Performance was validated through multi-faceted evaluation metrics covering pixel-level, contour-based, and object-based metrics. Experimental results demonstrate that gamma-based normalization, comprehensive augmentation, and hierarchical ensemble consistently outperform baseline configurations across multiple evaluation metrics. The final optimized pipeline achieved an F1-Score of 0.8070, making a significant improvement over the 0.7629 baseline. This work provides an extensible framework for operational satellite-based change detection and establishes a practical foundation for future ensemble-based architectures.