Pan-Sharpening Approaches Based on Unmixing of Multispectral Remote Sensing Imagery

Kurzfassung

Model based analysis or explicit definition/listing of all models or assumptions used in the derivation of a pan-sharpening 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. Most existing pan-sharpening methods are based mainly on the two models or assumptions: spectral consistency for high resolution multispectral data (physical relationship between multispectral and panchromatic data in a high resolution scale) and spatial consistency for multispectral data (so-called Wald’s protocol first property or relationship between multispectral data in different resolution scales). Additionally, it can be seen/shown easily that the following two popular groups of methods: spectral transformation (e.g. Intensity-Hue-Saturation (HIS), Principal Component Analysis (PCA), Gram–Schmidt orthogonalization (GS) and filtering (e.g. High Pass Filtering (HPF), Multi-Resolution Analysis (MRA)) based methods are based implicitly on a pure pixels assumption. Thus, their usage for mixed pixels (quite common situation in remote sensing applications) can lead to wrong image fusion results. Two methods, one based on a linear unmixing model and another one based on spatial unmixing, are described/proposed/modified which respect models assumed and thus can produce correct or physically justified fusion results. 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 or pan-sharpening is that a reference image is missing. Existing measures or so-called protocols are still not satisfactory because quite often the rationale or assumptions used 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 all fusion methods. Thus in this paper a comparison of the two earlier proposed/modified pan-sharpening methods together with some already existing model based methods and several other popular methods is performed. Experimental validation/verification is carried out in the urban area of Munich city for optical remote sensing multispectral data and panchromatic imagery of the WorldView-2 satellite sensor. The quality assessment of image fusion or pan-sharpening results is performed using a newly proposed measures based on common or general model error residuals and their combinations. Preliminary results confirm ideas of the author and show a great potential for future applications.