PRODUCING HIGH RESOLUTION SPECTRAL BANDS FROM LOW RESOLUTION MULTI-BANDS IMAGES, USING PRINCIPAL COMPONENT ANALYSIS “PCA” TECHNIQUE - Ali, Saleh; Mashi, Fouad; Ali, Suaada (Iraqi Journal of Science, 2024)
Image data fusion is the process of setting together information gathered bydifferent heterogeneous sensors, mounted on different platforms. This researchpresents an effective multi-resolution image data fusion methodology, which isbased on utilizing the Principal Component Analysis “PCA”. The first principalcomponent “PCA1” involves much of the variability in the spectral data; while thereminder PCAs contain the remaining variability in a descend order. The lowresolution multispectral bands are, firstly, resized (i.e. enlarged) into the highresolution “panchromatic” image size, then transformed into several PCAs. As firststep the panchromatic image is normalized to have the same number of gray levelsas the PCA1, then replacing the PCA1 of the low- resolution-multispectral image inthe PCA transformed domain. The high-resolution-multispectral images areproduced by inversely transform the modified PCA