Image data fusion is the process of setting together information gathered by
different heterogeneous sensors, mounted on different platforms. This research
presents an effective multi-resolution image data fusion methodology, which is
based on utilizing the Principal Component Analysis “PCA”. The first principal
component “PCA1” involves much of the variability in the spectral data; while the
reminder PCAs contain the remaining variability in a descend order. The low
resolution multispectral bands are, firstly, resized (i.e. enlarged) into the high
resolution “panchromatic” image size, then transformed into several PCAs. As first
step the panchromatic image is normalized to have the same number of gray levels
as the PCA1, then replacing the PCA1 of the low- resolution-multispectral image in
the PCA transformed domain. The high-resolution-multispectral images are
produced by inversely transform the modified PCA