The availability of imaging sensors operating in multiple spectral bands has led to the requirement of image fusion algorithms that would combine the image from these sensors in an efficient way to give an image that is more perceptible to human eye. Multispectral Image fusion is the process of combining images optically acquired in more than one spectral band. In this paper, we present a pixel-level image fusion that combines four images from four different spectral bands namely near infrared(0.76-0.90um), mid infrared(1.55-1.75um),thermal-infrared(10.4-12.5um) and mid infrared(2.08-2.35um) to give a composite colour image. The work coalesces a fusion technique that involves linear transformation based on Cholesky decomposition of the covariance matrix of source data that converts multispectral source images which are in grayscale into colour image. This work is composed of different segments that includes estimation of covariance matrix of images, cholesky decomposition and transformation ones. Finally, the fused colour image is compared with the fused image obtained by PCA transformation.

The availability of imaging sensors operating in multiple spectral bands has led to the requirement of image fusion algorithms that would combine the image from these sensors in an efficient way to give an image that is more informative as well as perceptible to human eye. Multispectral image fusion is the process of combining images from different spectral bands that are optically acquired. In this paper, we used a pixel-level image fusion based on principal component analysis that combines satellite images of the same scene from seven different spectral bands. The purpose of using principal component analysis technique is that it is best method for Grayscale image fusion and gives better results. The main aim of PCA technique is to reduce a large set of variables into a small set which still contains most of the information that was present in the large