# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
## 2 Materials and Methods
### Method
Because the quantitative analysis was checked on the native level of the images in this study, the following indices were selected to directly evaluate the spectral and spatial fidelity of the pansharpened images compared with the original MS images: 1) spectral assessment--the minimum (Min), maximum (Max), mean, the STDEV, coefficient of variance (CV), correlation coefficient (CC) between the four bands (the blue (B), green (G), red (R), and near-infrared band (NIR)), normalized difference vegetation index (NDVI, (NIR - Red)/(NIR + Red)), and normalized difference water index (NDWI, (Green - NIR)/(Green + NIR)) were used, which emphasize the spectral preservation; 2) spatial evaluation--the global spatial statistical indices such as the Moran's I (MoranI) and Geary's C (GearyC) were applied, which measure the spatial information; and 3) image quality--the EN and AG indices were used, which emphasize the quality of the pansharpened images. The NDVI is sensitive to green vegetation even for areas with low vegetation cover and was first used in 1973 by [PERSON] et al. [38]. The NDWI is sensitive to water features and eliminates the effects of soil and vegetation features [39]. Both indices have been widely used for studying global vegetation dynamics and monitoring plant canopy and soil moisture. In this study, the differences in the NDVI and NDWI between the pansharpened and original MS images were considered as the spectral quality indices. The MoranI index measures the local homogeneity by comparing the differences between neighboringpixels and the mean. The value range is \(+1\) to \(-1\); \(+1\), \(0\), and \(-1\) represent strong positive spatial autocorrelation, spatially uncorrelated data, and strong negative spatial autocorrelation, respectively. The GearyC index provides a measure of the dissimilarity within a dataset by comparing the differences between neighboring pixels and the standard deviation. The value range is \(0\) to \(2\); \(0\), \(1\), and \(2\) represent strong positive spatial autocorrelation, spatially uncorrelated data, and strong negative spatial autocorrelation, respectively. The EN surveys the amount of information in a pansharpened image. A larger EN corresponds to better performance of the pansharpening method but possibly a larger amount of noise in the pansharpened image. Thus, the EN is often used as a supplemental index [19; 40]. The AG measures the detail and texture of a pansharpened image. A larger AG corresponds to better performance of a given pansharpening method [19]. With the exception of the EN and AG indices, which were programmed in the IDL v8.2 software (Boulder, CO, USA), the other 10 indices were calculated in the ENVI v5.1 software. The mathematical expression and the ideal values were in accordance with previous studies [8; 9; 10; 11; 12; 13; 33] and the ENVI v5.1 software manual. The quality assessment was justified by the relative difference (change percentage = (the difference between the metric value of the original and pansharpened MS image/the metric value of the original MS image) \(\times\) 100%) between the metric values of the original MS image and the pansharpened data, rather than a statistical comparison, because only one scene of the GF-2 image was used. However, some studies indicated that it is necessary to perform a statistical comparison for image pansharpening research [41; 42]. For analyzing the effects of the pansharpening methods on different land covers, the first step was to segment the pansharpened and original MS images. Because the pansharpened images had more details than the original MS images, the segmentation results for the GS pansharpened images were used for 11 regions. Region 12 (reservoir region), Region 13 (canal region), and Region 14 (sea region) had only one type of land cover; thus, segmentation was not performed. The segmentation and classification processes were described in our previous work [43], which indicated that this approach provided high classification accuracy with GF-2 imagery.