Model based view at multi-resolution image fusion methods and quality assessment measures

Abstract

We propose to look at multi-resolution image fusion or pan-sharpening task from a model based perspective. Explicit definition of all models or assumptions used in the derivation of a fusion method allows us to understand the rationale or properties of existing methods and shows a way for a proper usage or proposal/selection of new methods better satisfying the needs of a particular application. Earlier mentioned property ‘better’ should be measurable quantitatively, e.g. by means of so-called quality measures. The difficulty of a quality assessment task in multi-resolution image fusion is that a reference image is missing. Existing measures or so-called protocols are still not satisfactory because quite often the rationale or assumptions are not valid or not fulfilled. From a model based view it follows naturally that a quality assessment measure can be defined as a combination of error model residuals using common or general models assumed in fusion methods. It is shown that most existing methods based on a spectral transformation or filtering are model based methods. Unfortunately, it was found out that they are based additionally on a pure pixels assumption. As they are applied for mixed pixels too that can lead to wrong fusion results. Model based view analysis shows which methods respect models assumed and thus can help to select methods which deliver correct or physically justified fusion results