<title>Model-based multispectral sharpening</title>

Abstract

Multispectral image sharpening involves enhancing the spatial characteristics of source multispectral imagery (MSI) acquired at low-resolution using a coregistered reference image acquired at a higher spatial resolution. As analysts become better trained in interpreting MSI and rely on spectral information for interpretation, it will be crucial that the sharpened products preserve the spectral information resident in the source MSI. We present a novel approach to sharpening which is explicitly designed to yield results which are consistent with the spectral information in the source MSI, i.e., when the sharpened MSI is filtered and decimated, the source MSI is reconstructed. Our approach involves developing explicit models that embody the assumed relationships among the source, reference and desired sharpened imagery. A sharpening algorithm is then posed as the solution to a constrained model-fitting problem. In this paper we discuss the general model-based image sharpening approach, and discuss a variety of possible models relating the reference and MSI datasets, and the resulting sharpening algorithms.