# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
## 4 Discussion
### Impact of Pansharpening Methods in Different Land Covers  
According to the results of the foregoing assessment based on regions, the behaviors of the pansharpening methods differed among the regions. The main reason for this may be that the different regions had varying proportions of land covers. Thus, it was necessary to analyze the effects of pansharpening methods in the different land covers. In cases where land cover accounted for \(>\)50% of a given region, the mIHS method resulted in minor changes to the Mean and STDEV values, and the CN and PCA methods resulted in major changes to them. In cases where shrub and grassland, bare soil, and canals accounted for only small areas in a given region, the behavior of the four pansharpening methods changed and sometimes exhibited contrary results. Additional experiments must be performed for more regions to further investigate this. The impervious surface of Region 1 had higher STDEV values in the original MS image than that of Region 2. The mIHS, GS, and PCA methods resulted in minor changes to the Mean value and major changes to the STDEV value for the impervious surface from Region 1 compared with the impervious surface from Region 2. This indicates that the higher values of the STDEV of the impervious surface in the original MS images resulted in higher-quality pansharpened images, which is consistent with the results of [PERSON] and [PERSON] (2014) [44]. However, the CN method produced opposite results. For the forest, Region 1 had higher STDEV values than Region 2 for all four bands in the original MS image. Only the GS and PCA methods resulted in minor changes to the Mean value for Region 1, and the mIHS and CN methods produced opposite results. All four pansharpening methods resulted in major changes to the STDEV values for Region 2 compared with those of Region 1. Compared with the impervious surface, the forest had smaller STDEV, CV, EN, and AG values, and the mIHS pansharpened image of the forest had the bigger difference in the Mean and STDEV values compared with the original MS bands than that of the impervious surface, which was consistent with the results at region scale. Therefore, the result that higher STDEV values of the land cover in the original MS images resulted in higher-quality pansharpened images was conditional for the pansharpening methods. This indicates differences in the pansharpened-image quality for different land covers [44]. However, in contrast to other land covers, the same number of spectral distortions in the canal and reservoir regions observed in a previous study was not observed in this study [15]. This should be further investigated. Nevertheless, it could be concluded that the proportion of different land-cover types and the change degree of pansharpening methods to different land covers in different regions could be used to roughly explain the impacts of pansharpening methods in different regions. With regard to preserving the MoranI and GearyI values (global spatial analysis), the mIHS method best preserved the spatial autocorrelation of most land covers, followed by the CN, GS, and PCA methods. However, for the aquaculture in Region 11 and the salt pan in Region 9, the results were the opposite, and for the impervious surface the CN method was the best and the PCA method was the worst. All four pansharpening methods increased the positive spatial autocorrelation of the impervious surface, forest, shrub and grassland, and bare soil in Region 3 and the winter wheat, unsown farmland, canal, and salt pan in Regions 5 and 10, to varying degrees. For preserving the MoranI value (image spatial autocorrelation preservation), all four pansharpening methods achieved the best result for the canal. For all four methods, the spatial autocorrelation of the forest, winter wheat, and sea water was altered by >90.0% after fusion. The reasons for this large increase should be investigated in the future.