Multispectral image fusion based on the common vector approach

Multispectral image fusion has attracted much attention in the area of computer vision based image processing for remote sensing, industrial automation, surveillance, medical and defense applications. The process carried out in image fusion is combining useful information stated on different channels related to the same scene. Since the proposed image fusion technique greatly improve the performance of image classification, segmentation and edge detection, a new solution is required to combine multispectral images in order to get more informative and good visualized one as well as preserving the important details behind them. By considering this fact, we have introduced a new image fusion approach based on the Common Vector Approach (CVA) concept. By examining the visual results, one can observe that CVA method presents good results as compared with Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Singular Value Decomposition (SVD).