Spatial PCA as a new method for image fusion

• Published in: 2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing pp. 090 - 094
• Main Authors: Shahdoosti, Hamid Reza, Ghassemian, H.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2012
• Subjects: Image fusion; multi-resolution analysis; Principal component analysis; Principal component analysis (PCA) transform; Remote sensing; Spatial resolution; Transforms
• ISBN: 9781467314787; 1467314781
• DOI: 10.1109/AISP.2012.6313724

An ideal fusion method preserves the Spectral information in fused image and adds spatial information to it with no spatial distortion. The PCA is a well-known pan-sharpening approach widely used for its efficiency and high spatial resolution. However, it can distort the spectral characteristics of the multispectral images. In this paper, we present a new fusion method based on the same concept. In conventional standard PCA method, PCA transform is applied to the spectral bands of multispectral images, but we applied PCA transform to the pixel blocks instead. Visual and statistical analyzes show that the proposed algorithm clearly improves the merging quality in terms of: RASE, ERGAS, SAM, correlation coefficient and UIQI; compared to fusion methods including, IHS, Brovey, PCA , HPF, HPM.