# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery
## 2 Materials and Methods
### Method  
In the present study, four pansharpening approaches (CN, PCA, GS, and mIHS) were used for sharpening the GF-2 image. The CN spectral sharpening method is an extension of the color normalization algorithm, which uses the PAN band to enhance the MS bands and can be used to simultaneously sharpen any number of bands and maintain the data type and dynamic range of the original image. The PCA spectral sharpening method applies the principal component transformation to convert the MS bands into principal components, and then an inverse principal component transformation is performed on the principal components, where the first principal component is displaced by the matched PAN band. The GS spectral method has a similar procedure to the PCA spectral sharpening method. The difference is that the first component of the GS transformation is replaced by the adjusted PAN band. Compared with the PCA method, the GS method is more accurate because it uses the spectral response function of a given sensor to simulate the PAN band. In this study, the average of the MS bands was used to simulate the low-spatial resolution PAN band. The mIHS sharpening method is an extension of the intensity-hue-saturation transformation and can sharpen four MS bands by iteratively repeating the sharpened process, each for three different MS bands. In this study, two iterations using red/green/blue and near-infrared (NIR)/red/green bands were selected to produce a sharpened image with all four bands. Additional details regarding these pansharpening approaches can be found in the literature [8; 9; 10; 11; 12; 13; 33], and the procedures are described in the ENVI v5.1 (Boulder, CO, USA) software (for the CN, PCA, and GS spectral sharpening methods) and ERDAS imagine v9.2 (Madison, AL, USA) software (for the mIHS method) manuals. A visual analysis of the pansharpened images, i.e., the conventional method, is necessary [1; 34; 35; 36], as the results and method ranking from the quantitative evaluation were typically inconsistent with those of remote-sensing experts [36]. Moreover, until now, no promising quantitative indices have been reported for the effective assessment of pansharpened images [2]. Although no standardization or universal adoption of the visual evaluation process has been devised until now [22], qualitative quality assessment is often performed according to the different band false-color combinations associated with color preservation and spectral consistency [34]. This is typically time-consuming and expensive, depends significantly on the experience of the interpreter, and cannot yet be automated by a computer system [16; 17; 28; 34; 35; 37]. Therefore, quantitative quality assessment is usually employed for measuring the spectral and spatial fidelity of pansharpened images [16; 35]. Several quantitative quality assessment indices have been reported. [PERSON] and [PERSON] (2006) divided 39 different indices into seven categories according to a mathematical analysis [18], and [PERSON] et al. (2016) categorized the existing quantitative quality assessment indices into three types [27]. In general, these indices should be objective, reproducible, and of a quantitative nature [34].