The image fusion technique was employed to generate a high-resolution image from a low-resolution one. A high scale was achieved by increasing the resolution of an image through interpolation, which relies on pixel neighbours. Multispectral images (low-resolution images with high resolution) from different satellites were utilized in this work for obtaining a high-resolution image. The crucial part of image fusion is image registration, which depends on obtaining a good high-resolution image. A scale-invariant feature transform (SIFT) was utilized to receive control points with an affine transform. Correct control points are determined depending on the scale of the image (downscaling followed by upscaling), which is necessary for stabilizing match control points between images. Control points were identified using the SIFT algorithm, which utilizes distance and angle to determine the correct match points. A different scale image is used to detect and correspond to the correct control points between the two images, thereby speeding up the process. Also, image scaling with contrast stretching was utilized for preprocessing to stretch brightness to obtain high-quality image features. A morphological operation is applying post-processing to images (scale, contrast stretching, and radiometric operation). Final image fusion is obtained using the colour model (luminosity, hue, and saturation). A metric criterion, root mean square error (RMSE), with peak signal-to-noise ratio (PSNR) is utilizing for determining the goodness of this process, which is used in image fusion.