**1. Introduction**

High spatial resolution satellite imagery is increasingly adopted globally to support spatial planning and monitoring of the built-up environment as evidenced by the proliferation of high-resolution commercial satellite sensors such as Pleiades, Worldview 1–4, *Satellite Pour l'Observation de la Terre* (SPOT) 6 and 7, Superview, and a wide range of high-resolution services and products derived from these sensors. Most modern satellite sensors carry onboard spectral bands of different spatial resolutions and spectral frequencies. In most instances, satellite sensors have narrow multispectral bands of relatively courser spatial resolution and a wide panchromatic band with higher spatial resolution. To facilitate better image visualization, interpretation, feature extraction, and land cover classification, an image fusion technique called pansharpening is used to merge the visible multispectral bands (red, blue, and green bands) and the panchromatic band to produce color images with higher spatial resolution [1–7]. The panchromatic band has wide spectral coverage in the visible and near-infrared wavelength regions. Pansharpening is aimed at producing a synthesized multispectral image with an enhanced spatial resolution equivalent to that of a panchromatic band [8–13].

Remote sensing using high-resolution satellites is now accepted as a dispensable tool that has the potential to support decision making in a wide range of social benefit areas, such as infrastructure and transportation management, sustainable urban development, disaster resilience, sustainable precision agriculture, and energy and water resources management. The demand for services and products that require users to discern features at high spatial and spectral precision has led most Earth observation service providers to develop geospatial products that use pansharpened satellite imagery that emerges from the fusion of the high spatial resolution panchromatic band and lower resolution multispectral bands [14–16].

Many studies have proved the value of pansharpened imagery in discerning geometric features from satellite imagery, cartography, geometric rectification, change detection, and in improving land cover classification accuracies [17–20]. Many pansharpening techniques have been developed over time to enable users to fully exploit the spatial and spectral characteristics available on most satellite systems. Pansharpening techniques aim to simultaneously increase spatial resolution while preserving the spectral content of the multispectral bands [11,20–22].

Pansharpening methods are classified into three broad categories: component substitution (CS)-based methods; multiresolution analysis (MRA)-based methods; and variational optimization (VO)-based methods. A new generation of pansharpening methods based on deep learning has been evolving in recent years. Component substitution methods rely on the application of a color decorrelation transform to convert unsampled lower-resolution multispectral bands into a new color system that differentiates the spatial and spectral details; fusion occurs by partially or wholly substituting the component that contains the spatial geometry by the panchromatic band and reversing the transformation [23]. Most studies report that while component substitution methods produce pansharpened products of good spatial quality the products suffer spectral distortions. Component substitution is considered more computationally efficient and robust in dealing with mismatches between the multispectral and panchromatic bands [10,23,24]. Typical examples of component substitution methods include principal component analysis (PCA) transform, Brovey's band-dependent spatial detail (BDSD), partial replacement adaptive CS (PRACS), Gram–Schmidt (GS) orthonormalization, and intensity-hue-saturation (IHS) transform. 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-shift

means (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(SANSA) is extensively used by government departments, municipalities, and public entities in South Africa to support spatial planning, crop, and natural resource monitoring. SANSA 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.
