# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
## 4 Discussion
### Impact of Pansharpening Methods in Different Regions and Imagries Acquired with Various Satellites  
Generally, the preservation of the statistical parameters of the image was necessary when the scholars wished to calculate the vegetation indices and perform classification according to the spectral-curve similarity among land covers [45]. Regarding spectral analysis (Min, Max, Mean, STDEV, CV, CC, NDVI, NDWI), the mIHS method resulted in minor changes to the statistical indices compared with the CN, GS, and PCA methods. However, for the vegetation-dominated regions, the PCA method resulted in minor changes to the Min values in the green and red bands for the forest area; in the blue, green, and NIR bands for the farmland area; and in the green, red, and NIR bands for the shrub and grassland area. For the vegetation-dominated and salt pan regions, the CN pansharpening method resulted in minor changes to the Max values in the blue and NIR bands and the NDVI for forest area; the blue and NIR bands for the farmland area; the blue, green, red, and NIR bands for the shrub and grassland area; and the blue band and the NDVI for the salt pan area. Except for an increase in the Min value in the red band for the GS pansharpening method and in the blue, green, and red bands for the PCA pansharpening method in the winter wheat and farmland area (Region 4), all four pansharpening methods reduced the Min values of all four bands, and the mIHS, GS, and PCA methods increased the Max values of all four bands in Regions 1-5. This is partly consistent with the results reported by [PERSON] (2008) for the effects of the PCA and mIHS methods on the Min and Max values [45]. Regarding only urban areas, the results of this study differed from those of [PERSON] (2008), who reported that the mIHS method reduced the correlation values of all the bands of a QuickBird urban image, and the PCA method increased them [45]. Overall, the foregoing results for the spectral indices indicate the superiority of the mIHS pansharpening method to the GS, CN, and PCA methods. This is consistent with the results of previous studies [7; 12], as well as previous results for the GS and PCA pansharpening methods [23; 30; 32; 36; 46; 50; 51]. It is also consistent with previous results for the mIHS and PCA pansharpening methods [48; 52] and the mIHS, GS, and PCA pansharpening methods [53]. However, it is inconsistent with the findings of [PERSON], who reported that the PCA method produced better results than the GS pansharpening method for the difference in the correlation coefficient and root mean square error (RMSE) values between the images before and after fusion in both IKONOS and QuickBird in Istanbul [30]. Additionally, it is inconsistent with the result of [PERSON] and [PERSON], who reported that the GS pansharpening method performed better for WorldView-2 images than the mIHS method with regard to the mean bias, median bias, mode bias, RMSE, and structure similarity index (SSIM) [54]. Sometimes, the quality assessment results for the different quality indices were contradictory; for example, the PC pansharpened IKONOS and QuickBird images were evaluated as the worst according to the spectral angle mapper index but the best according to the RMSE index, and overall, they were evaluated as the worst via a mean opinion score test [55]. The results of pansharpening the SPOT 4 or SPOT 5 MS images with the PAN IKONOSimage indicated that the best results were achieved by the CN method, followed by the GS and PCA methods, with regard to the correlation coefficient index (CCI), deviation per pixel index, and SSIM and the GS, PCA, and CN methods with regard to the RMSE [34]. For a WorldView-2 image, the GS method was worse than the mIHS method with regard to the CCI and SSIM, whereas it was better than the mIHS method with regard to the RMSE and signal-to-noise ratio [47]. With regard to preserving the MoranI and GearyI values (global spatial analysis), the best result for urban regions was achieved by the CN method, followed by the GS, mIHS, and PCA methods.