## 3 Methodology and Accuracy Assessment
### Multispectral Image Transform to CIELab Color System  
The RGB color system was designed in such a way that it includes nearly all primary colors and can be comprehended by human vision. Nevertheless, it is a tough task to deal with RGB color due to strong correlation between its components [29]. In this study, a uniform and complete color model, the CIELab color system, was used. In this uniform color system, a variation in the coordinates of the color component provides the same amount of variation in the luminance and saturation components [10]. Besides, this color space is projected to draw human vision, unlike the RGB and CMYK (cyan, magenta, yellow, black) color spaces [38]. The CIELab color system was used in the proposed Pan sharpening \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Description** & **References** \\ \hline IHS Method & In this system, the total amount of the brightness in one color is represented through intensity channel. The wavelength property of the color and the purity of the color are represented by hue and saturation respectively. & [30, 31] \\ \hline EHLERS (FFT-Enhanced & The fundamental idea of this method is modifying the Pan image in the way that it looks more similar to the intensity component of MS image. This method uses FFT (fast fourier transform) filtering for partial replacement instead of entire replacement of the intensity component. & [32] \\ \hline NNDiffuse (Nearest-neighbor diffusion-based) Method & This method, considers each pixel spectrum as a weighted linear combination of spectra of its sideward neighboring super pixels in the Pan sharpened image. Algorithm uses various factors like intensity smoothness (\(\sigma\)), spatial smoothness (\(\sigma\),\(s\)) and pixel size ratio for conducting the Pan sharpening & [33, 34] \\ \hline GIHS (Generalized IHS) & Directly applying of IHS method needs many multiplication and addition operations, which makes the Pan sharpening operation computationally inefficient. GIHS method develops a computationally efficient Pan sharpening method, which does not require coordinate transformation & [35] \\ \hline Gram-Schmidt Method & This method uses the Gram-Schmidt orthogonalization for converting the original low-resolution MS bands, which are linearly independent vectors, into a set of orthogonal vectors. The first vector in the orthogonal space is considered as simulated Pan image, which produced by weighted aggregation of the consecutive original MS bands. & [21, 22] \\ \hline Hyperspherical Color Sharpening Method & The Hyperspherical Color Sharpening method designed for WorldView-2 sensor imagery and can be applied to any MS data containing 3 bands or more. HCS approach is based on transforming original color space to hyperspherical color space. & [36, 37] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The brief review of state of the art Pan sharpening methods. approach in order to reduce the spectral distortion, while maintaining the color perception of human vision [39]. The design of the CIELab color system is based on [PERSON]'s theory, which indicates that only red (R), green (G), blue (B) and yellow (Y) are unique among the thousands of colors that are used to characterize the hue component [40]. Although, other colors can be produced using these unique colors (for example it is possible to obtain orange by mixing red and yellow), they (R, G, B and Y) can be described only with their own name. R, G, B and Y, with black (B) and white (W), constitutes a color system with six basic color properties and three opponent pairs: R/G, Y/B and B/W. The opponency idea rises from observation upon colors attributes, which proves no color could be characterized using both blue and yellow or red and green together [41]. A blue shade of yellow does not exist.