Preema Mole and M Mathurakan
Toc H Institute of Science and Technology, India

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 set. The paper compares different parameters namely, entropy, standard deviation, correlation coefficient etc. for different number of images fused from twoto seven. Finally, the paper shows that the information content in an image gets saturated after fusing four images.

Multispectral image fusion, pixel-level image fusion, principal component analysis, Grayscale image.