Ϯϵ ƚŚ Ed/KE> Z/K ^/E KE&ZE ;EZ^ ϮϬϭϮͿ Ɖƌŝů ϭϬ Ǧ ϭϮ ϮϬϭϮ &ĂĐƵůƚLJ ŽĨ ŶŐŝŶĞĞƌŝŶŐĂŝƌŽ hŶŝǀĞƌƐŝƚLJ ŐLJƉƚ C14. Estimation of the Optimal Set of Parameters for PAN-Sharpening of Satellite Images Based on the Non-Sub-sampled Contourlet Transform Mohamed R. Metwalli 1 , Ayman H. Nasr 1 , Osama S. Farag Allah 2 , S. El-Rabaie 2 , and Fathi E. Abd El-Samie 2 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 Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt ABSTRACT Recent studies show that hybrid PAN-sharpening methods using the Non-Sub-sampled Contourlet Transform (NSCT) and classical PAN-sharpening methods like the Intensity, Hue and Saturation (IHS), Principal Component Analysis (PCA), and Adaptive Principal Component Analysis (APCA), reduce the spectral distortion in the PAN- sharpened images. The NSCT is a shift-invariant multi-resolution decomposition. It is based on Non-Sub-sampled Pyramid (NSP) decomposition and Non-Sub-sampled Directional Filter Banks (NSDFB). We compare the performance of the APCA-NSCT using different NSP filters, NSDFB filters, number of decomposition levels, and number of orientations in each level on Spot4 data with spatial resolution ratio 1/2, and QuickBird data with spatial resolution ratio 1/4. Experimental results show that the quality of PAN-sharpening of remote sensing images of different spatial resolution ratios using the APCA-NSCT method is affected by NSCT parameters. For the NSP, the ‘maxflat’ filters have the best quality. For NSDFB the ‘sk’ filters have the best quality. Changing the number of orientations in the same level of decomposition in the NSCT has a small effect on both the spectral and spatial quality. The spectral and spatial quality of PAN-sharpened images mainly depends on the number of decomposition levels. Too few decomposition levels result in poor spatial quality, while excessive levels of decomposition result in poor spectral quality. Keywords: NSCT, PCA, APCA, PAN-sharpening. I. INTRODUCTION Currently, several remote sensing image fusion methods have been developed, including the frequently used Brovey method, IHS method, PCA method, and the recently developed Multi-Resolution Analysis (MRA) techniques. The MRA techniques include the Wavelet Transform (WT), filter banks, Laplacian pyramids, and redundant multi-resolution structures, such as the Undecimated Discrete Wavelet Transform (UDWT) and the “A Trous” Wavelet Transform (ATWT) [1]. Multi-resolution algorithms have the ability to apply hierarchical decomposition of an input image into successive coarser approximations. Such decomposition separates low- frequency components from high-frequency components in both the PAN and Multi-Spectral (MS) images in a scale-by-scale manner. Thus, the low-frequency components of MS images can be used without any modifications during the fusion process, and consequently, spectral distortion is limited. The missing spatial information in the MS image can be inferred from the high-frequency components of the PAN image [2]. The redundant multi- resolution decompositions do not require sharp digital filters and are not critically sub-sampled. The typical injection artifacts appearing in the images fused by means of conventional wavelet analysis, like ringing effects and canvas-like patterns disappear, when the images are fused using the redundant MRA. Therefore, the redundant MRA methods are particularly suitable for PAN-sharpening [1]. The Contourlet Transform (CT) provides a new representation system for image analysis. The CT is so called because of its ability to capture and link the discontinuity points into linear structures (contours). The two stages used to derive the CT coefficients involve a multi-scale transform and a local directional transform. The CT provides 2 l directions at each scale, where l is the number of required orientations. The CT lacks the shift- invariance and causes pseudo-Gibbs phenomena around singularities. The NSCT is a shift-invariant version of the CT, and it is based on the NSP and the NSDFB. Fusion based on the NSCT can use several methods for the injection of the image details. A number of hybrid methods have been developed to combine the best aspects of classical methods and multi-resolution transforms. Shah et al. [3] presented PCA-CT PAN-sharpening of high resolution (Ikonos and QuickBird) and medium resolution (Landsat7 ETM+) datasets. The NSCT provided better results than the sub-sampled CT. Xiaohui et al. [4] proposed an intensity component addition technique based on IHS transform and NSCT to preserve spatial resolution and color content. The IHS transform is applied on the MS image first, then the directional sub-band decomposition of the NSCT of the PAN image is added to the directional sub-band decomposition of the NSCT of the intensity component (I). Experiments showed that this method can improve spatial resolution and keep spectral information, simultaneously. Shah et al. [5] presented a combined adaptive PCA–NSCT approach for PAN-sharpening, which uses NSCT for the spatial transformation to capture intrinsic 271 978-1-4673-1887-7/12/$31.00 ©2012 IEEE