*3.2. Pan-Sharpening*

In the Pan sharpening procedure, the MS image should be resampled to the same pixel size of the Pan image before converting it to CIELab color space. In this research, the bicubic interpolation method was used to resample 2 m resolution Pleiades MS images into 50 cm resolution to match the pixel size of Pleiades Pan image. This resampled dataset is used in all Pan sharpening methods used in this research, including the proposed one. After converting the MS image from RGB to CIELab space, the Pan sharpening process continues with replacing the Pan image with the L\* component. Unlike the proposed method in [29] study, there is no need for color space conversion of Pan image in the proposed method, which leads to low computation and less data distortion. Before replacing the L\* band of MS with the Pan image, there is a histogram matching step that could be considered as preprocessing step. After resampling the MS image to the same size of Pan image and converting the MS image color space, the histogram of Pan image has to be matched with the histogram of L\* component in order to minimize the spectral differences [42]. For performing histogram matching task, mean and standard deviation normalizations were used [43]:

$$Pam^{HM} = (Pam - \mu\_{Pam})\frac{\sigma\_I}{\sigma\_{Pam}} + \mu\_{I\prime} \tag{8}$$

where *PanHM* stands for histogram matched Pan image, μ stands for mean and σ represents standad deviation. After these preprocessing steps, the L\* component is replaced with a Pan image. The Pan sharpened image is then produced by implementing inverse conversion of CIELab color system on the Pan\*a\*b\* image and results in a new MS image with high spatial resolution.

#### *3.3. Accuracy Assessment*

Several metrics were proposed to assess the accuracy of Pan-sharpened images that use the precise, high-resolution MS image as a reference image. In this research, the first seven metrics provided

in Table 2 were used for the spectral quality assessment, while the later four metrics were used for spatial quality assessment of the results. Although metrics provide important quantitative insights about the algorithm performance, qualitative assessment of the color preservation quality and spatial improvements in object representation is required. Thus, results obtained from the Pan sharpening algorithms were also evaluated with visual inspection.


