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. Index Terms—Image Fusion, Principal Component Analysis, High-Pass Filter and Spectral Quality. I. INTRODUCTION OR optical sensor systems, image spatial resolution and spectral resolution are contradictory factors. For a given signal to noise ratio, a higher spectral resolution (narrower spectral band) is often achieved at the cost of a lower spatial resolution. Some satellite sensors supply the spectral bands needed to distinguish features spectrally but not spatially, while other satellite sensors supply the spatial resolution to distinguishing features spatially. For many applications, the combination of data from multiple sensors provides more comprehensive information. Several commercial earth observation satellites carry dual-resolution sensors of this kind, which provide high spatial resolution panchromatic images and low spatial resolution multi-spectral images [1], [2]. 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 intensity hue saturation (IHS), Brovey transform (BT), principal component analysis (PCA) provide superior visual high-resolution multi- spectral images but ignore the requirement of high-quality synthesis of spectral information [1], [3]. While these methods are useful for visual interpretation, 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 [4]. In an attempt to overcome this limitation, another family of methods was developed. These operate 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 was at the beginning initiated by the High-Pass Filtering (HPF) method, which provides less spectral distortion [5]. 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 proposed fusion procedure has been assessed on two types of remote sensing data with different spatial and spectral properties. The experiments have shown that the proposed fusion model retains the spectral characteristics of the multi- spectral image and improves at the same time the spatial resolution of the fused image. II. IMAGE FUSION This section describes the image fusion by Principal Component Analysis (PCA) and High-Pass Filter (HPF) techniques. A. Principal Component Analysis (PCA) The PCA is a mathematical tool which transforms a number of correlated variables into a number of uncorrelated Mohamed R. Metwalli 1 , Ayman H. Nasr 1 , Osama S. Farag Allah 2 , and S. El-Rabaie 3 1 Data Reception, Analysis and Receiving Station Affairs Division, National Authority for Remote Sensing and Space Sciences, 23 Joseph Broz Tito st., El-Nozha El-Gedida, Cairo, Egypt 2 Department of Computer science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt 3 Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt Email: {moh_roshdym, srabie1 }@yahoo.com, aymanasr@hotmail.com Image Fusion Based on Principal Component Analysis and High-Pass Filter F 978-1-4244-5844-8/09/$26.00 ©2009 IEEE 63