A fast intensity-hue-saturation fusion approach via principal component analysis for ikonos imagery

To enhance spatial information of low resolution multi-spectral (RGB) image, the intensity-hue-saturation (IHS) approach is perfectly used to fuse the low resolution RGB image and the high resolution panchromatic (Pan) image by replacing intensity component with the high-resolution Pan image. Disadvantage of the mentioned approach is that color of a fused image is changed because the saturation component is changed or spectral of the low resolution RGB image and the high resolution Pan image is different, that is, spectral information of the fused RGB image is distorted. This problem is important for applying the fused image for classification. To solve this problem, in this paper, we employ the principal component analysis (PCA) transformation to extract information from the low resolution RGB image. In procedure of fusion method, the first principal component is used to adjust brightness of the high resolution Pan image. The intensity component from IHS transformation is replaced by the adjusted brightness high-resolution Pan image. The experimental results by using IKONOS imagery show that the proposed approach is better performance than the original IHS methods by improving spectral distortion and still correlating to the Pan image.