Quantitative Quality Evaluation of Pansharpened Imagery: Consistency Versus Synthesis

Pansharpening is the process of fusing a high-resolution panchromatic image and a low-spatial-resolution multispectral image to yield a high-spatial-resolution multispectral image. This is a typical ill-posed inverse problem, and in the past two decades, many methods have been proposed to solve it. Still, there is no general consensus on the best way to quantitatively evaluate the spectral and spatial quality of the fused image. In this paper, we compare the two most widely used and accepted methods for quality evaluation. The first method is the verification of the synthesis property which states that the fused image should be as identical as possible to the multispectral image that the sensor would observe at a higher resolution. This is impossible to verify unless the observed images are spatially degraded so that the original observed multispectral image can be used as reference. The second method is to use metrics that do not use a reference, such as the quality no reference (QNR) metrics. However, there is another property, i.e., the consistency property, which states that the fused image reduced to the resolution of the original multispectral image should be as identical to the original image as possible. This has generally been considered a necessary condition that does not have to imply correct fusion. Using real WorldView-2 and QuickBird data and a total of 18 component substitution and multiresolution analysis methods, we demonstrate that the consistency property can indeed be used to give reliable assessment of the relative performance of pansharpening methods and is superior to using the QNR metrics.