## 1 Introduction
Multiresolution analysis-based methods fuse the high frequencies inherent in the panchromatic band into the unsampled multispectral components through a multiresolution decomposition [23]. In contrast to component substitution methods, pansharpened products generated from multiresolution analysis are considered to produce superior spectral quality but are prone to spatial distortions, particularly when multispectral bands are misaligned with the panchromatic band [9; 10]. This is especially the case in multiresolution analysis techniques that apply transformations that are not shift-invariant to engender multiresolution analysis. Examples of multiresolution methods include high-pass modulation (HPM), Laplacian pyramid, discrete wavelet transform, and contourlet transform [23]. Such a transformation converts unsampled lower-resolution multispectral bands into a new color system that differentiates the spatial and spectral details and fusion occurs by partially or wholly substituting the component that contains the spatial geometry by the panchromatic band and reversing the transformation [23]. In recent years, a plethora of novel pansharpening methods have been developed to address the deficiencies of traditional image fusion algorithms. Most of the new pansharpening techniques are broadly clustered into generic categories such as component substitution (CS), multiresolution analysis (MRA), Bayesian, model-based optimization (MBO), sparse reconstruction (SR), and variational optimization (VO)-based methods [8; 9; 23; 25]. The spectral, radiometric, and spatial integrity of pansharpened imagery is critical for several quantitative remote sensing applications. To ascertain the spectral and spatial quality of pansharpened images, many quality metrics were developed. Preservation of spectral content is measured by statistical indicators such as correlation coefficient (CC), root means square error (RMSE), relative-shiftmeans (RM), the universal image quality index, structure similarity index (SSIM), and spectral angle mapper (SAM). A few quantitative measures were also developed to assess the spatial consistency of pansharpened imagery and these include the spatial correlation coefficient (SCC) and the spatial RMSE [10]. Pansharpened SPOT 6/7 and SPOT 5 imagery distributed by South Africa National Space Agency(SANASA) is extensively used by government departments, municipalities, and public entities in South Africa to support spatial planning, crop, and natural resource monitoring. SANASA has distributed pansharpened orthobundles and an annual wall-to-wall national 2.5 m mosaic for SPOT 5 from 2005 to 2012 and a biannual 1.5 m SPOT 6/7 mosaic from 2013 up to 2018. While these pansharpened products were successfully exploited by users, quality assessment of the pansharpened products was limited to visual inspections of the products. In most cases, users of pansharpened imagery require pansharpened products that retain the spectral content of the multispectral image and enhance their spatial detail. The objectives of this study are therefore to compare different pansharpening techniques by using quantitative image quality metrics and recommend the most ideal method with minimum spectral and spatial distortions for the operational production of the SPOT 6 mosaic.