The multispectral image combines monochrome and multiple bands from a sensor capturing the same area, with lower spectral resolution than the panchromatic (PAN) band. To boost the resolution, a pan-sharpening technique fuses the low-resolution bands with the high-resolution PAN band. This study focuses on using IHS transformation with cylindrical and cubical models to fuse multi-sensor data. The resulting hybrid images are well suited for human and machine analysis, enabling tasks like segmentation, feature extraction, and object identification. The fusion process's effectiveness is evaluated using various metrics. A modified Otsu method is introduced, detecting multiple objects instead of just one, optimizing computation resources for efficient feature extraction from pan-sharpened imagery. This research advances image processing techniques, particularly in land cover and land use (LCLU) classification. The study's primary contribution lies in comparing the effectiveness of the IHS method with the novel Otsu method tailored for LCLU classification. This provides a deeper understanding of multiple objects in imagery, enhancing land cover and land use analysis capabilities. The methodology proves valuable for resource-limited systems and lower-end configurations.