A development overview of a signal flow model for spectral data with a final assessment under a practical point of view
Concerning hyperspectral imaging, there is a need for handling big hyperspectral data. Especially merging data that was captured by different sources is very complex. Due to the different properties in resolution and other technological aspects, there are requirements for calibration, preprocessing and merging functions. This work is a summary of the approach that was developed in the framework of the Qualimess research project for handlings these demands. The invented declarative programming model is an innovative solution for implementing algorithms for all these aspects in an easy to handle signal flow model. The algorithm is designed in a way that it uses as little computing resources as possible, which is of high importance when processing and analyzing spectral cubes with standard computing systems. In this last development stage, previous investigations regarding the fusion of hyperspectral data captured by push-broom imaging systems are extended to other imaging technologies and hyperspectral 3D-imaging as well. Furthermore, there is a presentation of the developed graphical user interfaces that can be used for analyzing the data from an application-oriented point of view. These have the aim of extracting as much information as possible and presenting it to the user in a comfortable way. Finally, results in the framework of applied measurement under use of this signal processing method are presented for the first time. By evaluating this, an assessment can now be made of it under which circumstances these algorithms can be used in the context of industrial applications.