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The Image Fusion process is defined as the gathering of all important information from multiple images and their inclusion into fewer images, typically resulting in a single image. Medical imaging plays a crucial role in disease diagnosis and has significant applications in remote sensing for manipulating multisensory image data to obtain useful information. GFT (Global Fusion Transform) has been instrumental in obtaining results that are more suitable for human and machine interpretation. In order to address remote sensing challenges, an image fusion approach is proposed to enhance the visibility of the image, improve spatial resolution, and enhance the spectral information of the original images. A fully shift-invariant version of the contourlet transform, known as GFT, has been developed. This transform has demonstrated its efficiency in quantitative performance parameters such as mean square error, peak signal-to-noise ratio, and correlation coefficients. GFT has outperformedother decomposition techniques due to its shift invariance and its ability to extract features more effectively, facilitated by its complex structure.