# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
## 3 Results
### Quantitative Quality Assessment by Region  
In this study, 14 regions were employed for the quantitative assessment. Owing to length limitations, only quality assessment results of the urban region (Region 1) are displayed in Figure 4.  
All four pansharpening methods reduced the Min values of the employed regions compared with the original MS bands (see Figure 4), with only one exception: the farmland (Region 4), for which the PCA method increased the Min values of the blue, green, and red band, and the GS method increased the Min value of the red band. In contrast to the Min values, the GS, PCA, and mIHS pansharpening methods increased the Max values in all four bands of the regions compared with the original MS bands (see Figure 4). The CN method reduced the Max values in all four bands of the regions compared with the original MS bands, with only one exception (the urban region, i.e., Region 1, shown in Figure 4). This means that the pansharpening expands the range of spectral values of four bands. In general, the four pansharpening methods changed the Min value less than the Max value of the regions. The CN method obtained the biggest difference in Min values of all four bands in the urban, forest, shrub and grassland, farmland, and salt pan regions, and the PCA method obtained the biggest difference in Max value of the regions. The mIHS method exhibited the smallest difference in the Min NDVI values in the forest (0.3%), shrub and grassland (0.1%), and farmland region (\(-\)0.3%), and the smallest difference in the Max NDVI values in the forest (\(-\)10.6%), shrub and grassland (\(-\)0.5%), and farmland  
Figure 4: The relative difference (change percentage) compared to the original multispectral (MS) image) of quantitative quality indexes for four pansharpened images in Region 1. (\(-0.2\%\)) regions, followed by the CN, GS, and PCA method. For the NDWI, the CN and mIHS methods exhibited the smaller difference in the Min and Max values compared with the original NDWI values. For the Mean value, compared with the GS, CN, and PCA methods, the mIHS pansharpening method exhibited the smallest difference for all four bands in all regions, followed by the GS, PCA, and the CN method (see Figure 4). Compared with the mIHS and CN methods, the GS and PCA methods produced the bigger difference in the Mean NDVI value in the forest, and farmland regions, which indicated that the NDVI values from the GS and PCA pansharpened images should be calibrated before use. For the STDEV value, the mIHS pansharpening method exhibited the smallest difference for all four bans for the regions (see Figure 4), with one exception: the farmland region. For the CV value, the mIHS pansharpening method exhibited the smallest difference for all four bands in all regions (see Figure 4), with one exception: the farmland region. For preserving the CV value of all four bands, the mIHS method was best, followed by the GS, CN, and the PCA method. However, for preserving the CV values in NDVI and NDWI indices, the CN method was best in the shrub and grassland, farmland, and urban regions, followed by the mIHS, GS, and the PCA method. For the EN, the mIHS method exhibited the smallest difference in the NIR band for all regions (\(-0.9\%\) in salt pan region), the smallest difference in the blue band for the city, forest, shrub and grassland, and farmland regions (\(-1.8\%\) in farmland region), the smallest difference in the green band for the urban, forest, shrub and grassland, and salt pan regions (\(0.3\%\) in salt pan region), and the smallest difference in the red band for the forest, shrub and grassland, and salt pan regions (\(0.0\%\) in shrub and grassland region). For preserving the EN value of all four bands, the mIHS method was best, followed by the CN method (see Figure 4). The PCA method was worst. In general, the change of EN values of the shrub and grassland, farmland, and salt pan regions by all pansharpening methods was less than that of urban, and forest regions.