Radiometric assessment of four pan-sharpening algorithms as applied to hyperspectral imagery

Abstract

Pan-sharpening - fusing the spatial and spectral information between panchromatic (PAN) and multispectral (MSI) or hyperspectral (HSI) imagery of a common scene is a hot topic in remote sensing due to a wide range of applications such as target detection, vegetation monitoring, and subsurface detection (e.g. landmines), among others. However, the focus of panchromatic sharpening is generally placed on visual quality of the resulting image and image-wide summary spectral accuracy metrics. Here we are interested in radiometrically accurate panchromatic sharpening of hyperspectral imagery with particular emphasis on spectral algorithm performance. Four pansharpening algorithms are applied to hyperspectral imagery and evaluated for spectral/radiometric fidelity. Two datasets from SHARE2012 were used: one which features rural scene elements and one which features an urban scene. Target detection was also performed to evaluate algorithm sharpening performance. We find that although visually the performance of the four algorithms were roughly similar, they differ in spectral/radiometric fidelity as well as performance in ACE target detection.