## 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 coarser 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; 2; 3; 4; 5; 6; 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; 9; 10; 11; 12; 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; 15; 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; 18; 19; 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; 21; 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.