High-resolution satellite imagery is crucial for effective urban planning and infrastructure monitoring. However, commercial high-resolution data is often cost-prohibitive, while open-source multispectral imagery, such as Sentinel-2 or Landsat, lacks the spatial detail required for accurate urban feature extraction. This study addresses this limitation by proposing a practical application of a multi-frame super-resolution approach. The methodology integrates the Projection Onto Convex Sets (POCS) algorithm with highly precise sub-pixel registration via the Enhanced Correlation Coefficient (ECC) method and edge-preserving bilateral filtering. This combination allows for the effective extraction of sub-pixel information from multiple low-resolution frames while significantly mitigating severe ringing artifacts. The proposed framework was practically evaluated on dense urban environments using a sequence of multispectral Sentinel-2 imagery covering Kyiv, Ukraine. The experimental results demonstrate a 310.81% improvement in the Spatial Correlation Coefficient (SCC) and an increase in the Structural Similarity Index (SSIM) to 0.5708 when compared to the standard bicubic interpolation baseline, also outperforming the classical Iterative Back-Projection method by 26.76% in absolute spatial fidelity. Visual assessments confirm significant enhancements in edge preservation and structural clarity of complex building footprints and road networks. In conclusion, this mathematically robust and physically-interpretable approach provides a highly effective, cost-efficient solution for upgrading open-source satellite imagery, thereby substantially improving the interpretability of complex urban structures for cartographic, cadastral, and remote sensing applications.
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