Strategies for Merging Hyperspectral Data of Different Spectral and Spatial Resoultion

Abstract

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.