Adjustable Transforms of ISH and PCA for QuickBird Panchromatic and Multispectral Images

The adjustable image fusion methods are presented based on intensity-hue-saturation (IHS) transform and principal component analysis (PCA) transform in this paper. IHS and PCA can quickly merge huge amounts of remote sensing images and can preserve both spectral and spatial information. On the basis of summarized adjustable transform algorithm and transform processing procedure, the methods have been applied to merge QuickBird low-resolution multispectral image and high-resolution panchromatic image of the part area in JiaoDong Peninsula, one of the most prosperous economic region in China. The validity of each transform method is estimated by the quantity with the spectral authenticity, and the quality with the spatial texture of the resulting fused images respectively. Experimental results show that images merged by IHS showed higher spatial resolution and better spectral features than the original QuickBird imagery. Images merged by PCA also showed higher spatial resolution, but lost some spectral information. Therefore, IHS is more efficient and highly accurate for merging QuickBird images.