# Quality Assessment by Region and Land Cover of Sharpening Approaches Applied to GF-2 Imagery  
[PERSON]  
1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (C.H. ); [EMAIL_ADDRESS] (H.L.) 2  
[PERSON]  
1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (C.H. ); [EMAIL_ADDRESS] (H.L.) [PERSON]  
1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (C.H. ); [EMAIL_ADDRESS] (H.L.) 2  
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level [7]. The details of popular and state-of-the-art pansharpening methods in these three levels were reviewed and explained by [PERSON] [7]. Pansharpening methods at the pixel level are the mainstream approaches and are popularly applied in practice. Some have been embedded in commercial remote-sensing image processing systems such as ENVI, ERDAS Imagine, and PCI Geomatica. The representational methods are the modified intensity-hue-saturation (mIHS), principal component analysis (PCA), color spectral sharpening (CN), and Gram-Schmidt spectral sharpening (GS) approaches [8, 9, 10, 11, 12, 13]. However, numerous problems make pansharpening challenging, and no single pansharpening method can deal with all of them [14, 15]. The widespread application of many different pansharpening approaches has increased the importance of evaluating the quality of pansharpened images [7, 16]. Existing pansharpened image quality assessment methods employ many different quality metrics for both qualitative (visual analysis) and quantitative (objective analysis with and without a reference image) evaluations [7, 17]. The available quality metrics were reviewed in previous studies [7, 16, 18, 19, 2]. Of course, the classification accuracy can also be used to compare the quality of the pansharpened images derived from the various pansharpening methods [6, 20, 21, 22, 23]. However, this quality assessment method is seldom used, because it cannot provide the spectral, spatial, and radiometric quality of the pansharpened image separately. Quantitatively assessing image pansharpening is challenging because of the different application demands and the absence of a clearly defined ideal reference image [7, 24, 25]. There are two main approaches for evaluating the quality of the pansharpened image without a reference image. The first approach considers the original MS image as a reference and evaluates the quality of the pansharpened image by calculating the differences between the original MS image and the downsampled pansharpened MS image using the proposed spatial and spectral quality indices. This approach operates on an image at the downsampled scale, but the quality of the pansharpened images derived from the different pansharpening methods depends on the spatial scale and the imaging sensor, and the performance of the assessment at the downsampled scale may not be the same as that at the full scale [26]. Quality assessment of pansharpened image through downsampling of the original PAN and MS images is not suitable for high-resolution images; quality assessment should be performed in full resolution [27]. The second approach focuses on the relationships among the original and pansharpened images. This approach directly operates on the image at the native scale, but the quality metrics may affect the assessment results [7].