Image fusion based on principal component analysis and high-pass filter

Abstract

Several commercial earth observation satellites carry dual-resolution sensors, which provide high spatial resolution panchromatic image and low spatial resolution multi-spectral image. Image fusion techniques are therefore useful for integrating a high spectral resolution image with a high spatial resolution image, to produce a fused image with high spectral and spatial resolutions. Some image fusion methods such as IHS, PC and BT provide superior visual high-resolution multi-spectral images but ignore the requirement of high-quality synthesis of spectral information. The high-quality synthesis of spectral information is very important for most remote sensing application based on spectral signatures, such as lithology, soil and vegetation analysis. Another family of image fusion techniques such as HPF operates on the basis of the injection of high-frequency components from the high spatial resolution panchromatic image into the multi-spectral image. This family of methods provides less spectral distortion. In this paper we propose to integrate between the two families PCA and HPF to provide pan sharpened image with superior spatial resolution and less spectral distortion. The experiments have shown that the proposed fusion method retains the spectral characteristics of the multi-spectral image and improves at the same time the spatial resolution of the fused image.