Handling big measurement data is increasingly developing into a task with high requirements regarding efficiency and ensuring unaltered accuracy. Especially in the framework of spectral measurement, there is a rapidly increasing demand caused by rising availability of constantly improved sensor systems with better resolution and sensitivity. By using spectral imaging, an object can be measured including its spectral and spatial information. Respecting the claims on information given by a measurement result, for example from the field of quality assurance, the experimental setup that is used in this work should reach a spectral range from 220 nm up to 1700 nm. Because of technical limitations, more than one push-broom imaging system is necessary for measuring in such a wide spectral range. This paper deals about the evaluation and the further work concerning the techniques for matching spectral cubes acquired by different sources. It ties in with previous work, which laid down the fundamental ideas for handling those special kinds of big data sets. The developed algorithm is able to handle hyperspectral data from a multiple push-broom imaging system and the integrated calibration strategy ensures the correction with respect to geometric and chromatic aberrations. Further on, the experimental data will be compared to find the promising approach, depending on the case of application. A short survey of the analysis is also included and a simple idea for decreasing the effect of motion blur based on wavelet transformation was realized as well. The paper closes a chapter of investigations for merging spectral cubes, acquired by a multiple imaging prototype system with an efficient result.