Spot, have this capability, and bundle (PAN+MS) products from these systems can be used directly as the input for Pan sharpening. An ideal Pan sharpening algorithm leads to the best performance in spatial and spectral domains by keeping the spatial resolution of a Pan image and preserving the spectral characteristics of an MS image. Launching of VHR sensors led to the appearance of diverse Pan sharpening methods in recent decades [5; 6; 7]. In addition, Pan sharpening is a primary image enhancement step for many remote sensing applications, such as object detection [8], change detection [9], image segmentation and clustering [10; 11], scene interpretation and visual image analysis [12]. Commonly, image fusion can be classified into three levels--pixel level, feature level and decision or knowledge level--while the Pan sharpening is categorized as a sub-pixel level process [13; 14]. Pan sharpening algorithms can be divided into four groups: (1) rationing methods; (2) injection-based methods; (3) model-based methods; and (4) component substitution (CS) methods. Of these methods, CS algorithms are more practical because of their calculation speed and performance compatibility. The CS methods can be categorized into four classes according to the transform matrix used in the algorithm; which are principle component analysis (PCA) [15; 16], intensity-hue-saturation (IHS) [7; 17], Gram-Schmidt (GS) [18; 19] and generalized component substitution (GCS) [5; 20]. The common and general limitation of all CS-based methods is the distortion in spectral characteristics when compared to original MS image [21; 22]. This research proposes a robust CS method for Pan sharpening the Pleiades VHR satellite images with the aim of enhanced spatial resolution and reduced spectral distortion. The principle of the proposed method is similar to the IHS method, where a uniform CIELab color space based on human eye spectral response is used instead of IHS color space [23]. The CIELab color space has been used for different image processing tasks. [PERSON] and [PERSON], 2010 [24], investigated the performance of CIELab color space on the application of unsharp masking and fuzzy morphological sharpening algorithms. In the study of [25], the experiments of the content-based image retrieval (CBIR) were used to evaluate the performance of CIELab and the other three color spaces (RGB, CIELuv and HSV) on an image retrieval process. In addition, CIELab color space was used to help different image segmentation tasks [26; 27]. In a previous Pan sharpening research, color normalization-based on CIELab color space aided the image fusion algorithm with sharpening a Hyperion hyperspectral image with an Ikonos Pan image using the spectral mixing-based color preservation model [28]. In another study, a remote sensing image fusion technique using CIELab color space was proposed by [PERSON] et al. [29]. In that study, the authors improved the performance of image fusion techniques by combining non-subsampled shearlet transform and pulse coupled neural network. However, this approach is computationally complicated and there is lack of a specific satellite dataset. Although the CIELab method is used in different image processing tasks applied on natural and satellite images, evaluation of its performance on the Pan sharpening process is limited and there is no detailed evaluation of this method on VHR image Pan sharpening by considering the different landscape characteristics and with use of spatial and spectral metrics in addition to visual interpretation yet, to our knowledge. This research focused on proposing a robust, CIELab-based Pan sharpening approach and aimed to fill the abovementioned gap in detailed investigation and accuracy assessment of CIELab-based Pan sharpening in the literature. In this research, results from the proposed method were compared with the results from the six well-known methods, which are Ehlers, Generalized IHS, IHS, Gram-Schmitt, HCS and NNDiffuse methods.