Four Panchromatic and Multispectral Image Fusion Methods in Image Processing

This article explores four prominent image fusion techniques for combining panchromatic and multispectral imagery: IHS (Intensity-Hue-Saturation), PCA (Principal Component Analysis), Wavelet Transform, and a hybrid Wavelet-PCA approach. Each method's implementation typically involves key processing steps - IHS transformation requires converting RGB to IHS color space and replacing intensity components, PCA fusion utilizes principal component substitution from covariance matrix analysis, while wavelet methods apply multiresolution decomposition using filters like Daubechies. The hybrid method combines wavelet's detail preservation with PCA's spectral enhancement. We evaluate fusion quality through computational metrics including Information Entropy (measuring texture richness via histogram distribution analysis) and Q4 index (assessing spectral fidelity through hypercomplex correlation calculations). These quantitative assessments help determine each method's performance characteristics and optimal application scenarios, providing valuable guidance for selecting appropriate fusion strategies in remote sensing and computer vision applications.