Strategies for Merging Hyperspectral Data of Different Spectral and Spatial Resoultion

Increasing applications for hyperspectral measurement make increasing demands on the handling of big measurement data. Push broom imaging is a promising measurement technique for many applications. The combined registration of hyperspectral and spatial data reveal a lot of information about the measurement object. An exemplary well-known further processing technique is to extract feature vectors from such a dataset. For increasing quality and quantity of possible information, it is advantageously to have a spectral wide range dataset. Nevertheless, different spectral data mainly needs different imaging systems. A major problem in using hyperspectral data from different hyperspectral imaging systems is the combination of those to a wide range data set, called spectral cube. The aim of this work is to show which methods are principal conceivable and usable under different circumstances for merging such datasets with a profound analytical view. In addition, some work that was done in the theory and the design of a calibration model prototype is included.