# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
Despite the questionable assumption of scale invariance for pansharpening performance [26], several reliable quality indices can be used, including global statistics such as the mean, standard deviation (STDEV), entropy (EN), and average gradient (AG) [18, 19, 22]. Quality assessment of the different image pansharpening methods is traditionally performed for the whole scene through quality metrics calculated pixel-by-pixel or region-by-region. However, this method is not only tedious and slow but also unsuitable for the assessment of regions that contain complementary information in PAN and MS images [28]. Additionally, it cannot evaluate the similarities of features and land covers between the original and pansharpened MS images. Hence, quality assessment by land cover is a promising approach that facilitates the analysis of the effects of pansharpening methods across different features and land covers [15]. However, to date, research has only focused on a few types of land covers, such as soil, water, and crops [15], and the responses of different land covers to pansharpening methods require further study. Therefore, it is necessary to evaluate the performance of the pansharpening process for more land covers. Spectral distortion is a significant issue when existing pansharpening methods are used with imagery captured by new satellites [29]. This may be due to the different spectral ranges, different spectral resolutions of the MS bands, and the difference in the spectral ranges between the PAN and MS bands. Hence, quality assessment of existing pansharpening methods applied to images recently captured by new satellites should also be performed. Many researchers have evaluated the performance of pansharpening methods on Landsat, Sentinel-2, SPOT 5, QuickBird, IKONOS, WordView-2, and Geoye-1 images [12, 13, 15, 30, 31], but few studies have been performed for Gaofen 2(GF-2) imagery. The GF-2 satellite, which was launched in August 2014, is equipped with two MS scanners with a PAN band that has a spatial resolution of 1 m and four MS bands with a spatial resolution of 4 m (Table 1). There is an urgent need for high-quality pansharpened GF-2 images to satisfy different application purposes such as tree species classification, aquaculture area mapping, urban water body monitoring, and urban planning. Similar to other high-resolution satellites such as IKONOS, QuickBird, and WorldView-2, the spectral range of the PAN band of the GF-2 satellite is wider than that of MS bands (Table 1), and except for the fact that the blue and red band of GF-2 is similar to that of QuickBird, and WorldView-2, and its PAN band is similar to that of Quickbird and IKONOS, and its near infra-red band is similar to that of WorldView-2, they are slightly different in other bands; hence, the commonly used pansharpening methods easily produce color distortion [32]. Thus, the objective of this study was to evaluate several widely used pansharpening approaches applied to GF-2 imagery by region (14 regions) and land cover (11 types of land cover) with several reliable quality indices, (1) to determine the impact of pansharpening methods in different land covers and (2) to identify the optimum method for pansharpening GF-2 imagery. The quality of the pansharpened image is inconsistent among the different regions and different land covers because they have the different spectral and spatial information. This paper is attempted to assess the difference of fusion quality over different regions and land covers, and find out which pansharpening method is more effective for which land cover.