This paper introduces a ground-breaking methodology for enhancing the quality of satellite imagery through smoothing filter-based intensity modulation fusion technique. By integrating the Smoothing Filter-Based Intensity Modulation (SFIM) algorithm, our approach achieves a harmonious balance between spatial resolution and spectral fidelity, addressing the inherent challenge of optimizing both aspects simultaneously. Unlike traditional fusion methods that rely solely on global parameters derived from overall spatial and spectral characteristics, our approach incorporates precise building segmentation to tailor fusion parameters independently for built-up and non-built-up areas. This adaptive adjustment ensures optimized fusion outcomes that accurately represent the distinct characteristics of each region. In addition to the segmentation-guided parameter adjustment, and introduced gradient simulation operation to further refine the spatial characteristics of multispectral images. By extracting gradient values from the multispectral data, we enhance the spatial detail and coherence, resulting in a more comprehensive representation of the scene. Experimental validation conducted on Jilin-1 satellite imagery demonstrates the superior performance of our method compared to conventional methods of fusion techniques, particularly in terms of spatial resolution, multispectral fidelity, and information content. The outcomes of this study hold significant implications for a wide range of applications, including urban planning, environmental monitoring, remote sensing, and disaster management.