A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery

Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images. Selecting an appropriate algorithm that maintains the spectral spatial information content of input images is a challenging task. This review paper investigates wide range algorithms, including 41 methods. For this purpose, were categorized as Component Substitution (CS-based), Multi-Resolution Analysis (MRA), Variational Optimization-based (VO), Hybrid tested on collection 21 case studies. These include from WorldView-2, 3 & 4, GeoEye-1, QuickBird, IKONOS, KompSat-2, KompSat-3A, TripleSat, Pleiades-1, Pleiades with aerial platform, Deimos-2. Neural network-based excluded due their substantial computational requirements for operational mapping purposes. The evaluated based four Spectral three Spatial quality metrics. An Of Variance (ANOVA) was statistically compare pan-sharpening categories. Results indicate MRA-based performed better in terms quality, whereas most Hybrid-based had highest CS-based lowest results both spectrally spatially. revisited version Additive Wavelet Luminance Proportional method Generalized IHS Best Trade-off Parameter Weights showed quality. generally fastest run-time, majority belonging MRA VO categories relatively long run times.