@article{PomrehnKleinKolbetal.2019,
  author    = {Christoph Pomrehn and Daniel Klein and Andreas Kolb and Peter Kaul and Rainer Herpers},
  title     = {Supervised classification of monomodal and multimodal hyperspectral data in vibrational microspectroscopy: A comprehensive comparison},
  series   = {Chemometrics and Intelligent Laboratory Systems},
  volume    = {184},
  publisher = {Elsevier},
  issn      = {0169-7439},
  doi       = {10.1016/j.chemolab.2018.11.013},
  url       = {https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-43308},
  pages     = {112 -- 122},
  year      = {2019},
  abstract  = {The application of Raman and infrared (IR) microspectroscopy is leading to hyperspectral data containing complementary information concerning the molecular composition of a sample. The classification of hyperspectral data from the individual spectroscopic approaches is already state-of-the-art in several fields of research. However, more complex structured samples and difficult measuring conditions might affect the accuracy of classification results negatively and could make a successful classification of the sample components challenging. This contribution presents a comprehensive comparison in supervised pixel classification of hyperspectral microscopic images, proving that a combined approach of Raman and IR microspectroscopy has a high potential to improve classification rates by a meaningful extension of the feature space. It shows that the complementary information in spatially co-registered hyperspectral images of polymer samples can be accessed using different feature extraction methods and, once fused on the feature-level, is in general more accurately classifiable in a pattern recognition task than the corresponding classification results for data derived from the individual spectroscopic approaches.},
  language  = {en}
}