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Abstract
Preservation of spectral and spatial information is an important requirement for most quantitative remote sensing applications. In this study, we use image quality metrics to evaluate the performance of several image fusion techniques to assess the spectral and spatial quality of pansharpened images. We evaluated twelve pansharpening algorithms in this study; the Local Mean and Variance Matching (IMVM) algorithm was the best in terms of spectral consistency and synthesis followed by the ratio component substitution (RCS) algorithm. Whereas the IMVM and RCS image fusion techniques showed better results compared to other pansharpening methods, it is pertinent to highlight that our study also showed the credibility of other pansharpening algorithms in terms of spatial and spectral consistency as shown by the high correlation coefficients achieved in all methods. We noted that the algorithms that ranked higher in terms of spectral consistency and synthesis were outperformed by other competing algorithms in terms of spatial consistency. The study, therefore, concludes that the selection of image fusion techniques is driven by the requirements of remote sensing application and a careful trade-off is necessary to account for the impact of scene radiometry, image sharpness, spatial and spectral consistency, and computational overhead.
Highlights
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
While the IMVM and ratio component substitution (RCS) pansharpening methods showed superior performance compared to the other fusion methods such as the PANSHARP, modified intensity hue saturation (MIHS), GRS, wavelet transform, Bayesian, and Ehlers fusion technique (EHLERS) pansharpening techniques, the results of this study clearly show the credibility of these methods in terms of preservation of spectral and spatial information
Image quality metrics were used to evaluate the performance of twelve image fusion techniques
Summary
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. 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 [1,2,3,4,5,6,7]. The panchromatic band has wide spectral coverage in the visible and.
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