# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
## 5 Conclusions  
Most traditional image pansharpening quality assessment approaches have been performed for the whole image or regions such as urban or agricultural areas. However, major differences in a given region or land cover can be minimized by small differences in another region or land cover. Thus, it is necessary to evaluate the performance of the pansharpening process for different land covers and quantify the quality of the process for different land covers. This enhances our understanding of the effects of pansharpening methods for different land covers. Many quality metrics have been used to evaluate the quality of the pansharpened image at the downsampled scale, but the quality of the pansharpened images obtained using the different pansharpening methods depends on the spatial scale and the imaging sensor. Additionally, the performance of the assessment at the downsampled scale may not be the same as that at the full scale. Moreover, when the existing pansharpening methods are used with imagery captured by new satellites, spectral distortion is a significant issue. Hence, quality assessment of the existing pansharpening methods for images captured by new satellites should be performed. In this study, we evaluated the widely used mIHS, GS, CN, and PCA pansharpening methods for GF-2 imagery with regard to land cover, using several reliable quality indices at the native spatial scale without reference. Both visual and quantitative analysis by region indicated that all four approaches satisfied the demands for improving the spatial resolution of the original GF-2 MS image, and the mIHS pansharpening method produced better results than the GS, CN, and PCA methods by preserving the colors of the original MS image. The mIHS, GS, CN, and PCA methods performed well for some regions, such as urban, and agricultural regions, but not for others, such as forest, and shrub and grassland regions. The inconsistency in the quality of the pansharpening methods across various land covers was highlighted in this study. With regard to the Mean and STDEV values (spectral analysis), the mIHS method typically performed the best for most land covers, followed by the GS, PCA, and CN methods. With regard to the MoranI and Gearyl values (global spatial analysis), the mIHS method best preserved the spatial autocorrelation for most land covers, followed by the CN, GS, and PCA methods. With regard to the EN and AG values (image quality analysis), from the viewpoint of increasing the image information and texture of the pansharpened image, the PC method added the largest amount of information and detail to the pansharpened images for most land covers (with the exception of the salt pan), and the mIHS method added the smallest amount of information and detail to the pansharpened images. For all four pansharpening methods, after pansharpening, the spatial autocorrelation of the forest, winter wheat, and sea water was altered by \(>\)90.0%, and more information and detail was injected into the pansharpened images for the canal, forest, and sea water. This needs to be studied further. Overall, the mIHS method is recommended for pansharpening GF-2 imagery. In this study, the quality of pansharpening methods was evaluated with regard to the land cover, by calculating the quality indices pixel-by-pixel. In the future, assessments should be performed through a statistical comparison of the individual image object of each land cover using more pansharpening approaches with more image-quality metrics across a larger number of regions with fragmented land cover and terrain. Moreover, effects of atmospheric and topographic correction on the pansharpened images should be considered. Conceptualization, Q.L. ; Data curation, C.H. ; funding acquisition, Q.L. ; methodology, Q.L. and C.H. ; validation, H.L. ; writing--original draft, Q.L. ; writing--review and editing, Q.L., C.H. and H.L. All authors have read and agreed to the published version of the manuscript.