Supervised classification of monomodal and multimodal hyperspectral data in vibrational microspectroscopy: A comprehensive comparison

Christoph Pomrehn, Daniel Klein, Andreas Kolb, Peter Kaul, Rainer Herpers
Chemometrics and Intelligent Laboratory Systems, Volume 184, page 112--122 - 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.

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BibTex references

@Article{PKKKH19,
  author       = {Pomrehn, Christoph and Klein, Daniel and Kolb, Andreas and Kaul, Peter and Herpers, Rainer},
  title        = {Supervised classification of monomodal and multimodal hyperspectral data in vibrational microspectroscopy: A comprehensive comparison},
  journal      = {Chemometrics and Intelligent Laboratory Systems},
  volume       = {184},
  pages        = {112--122},
  year         = {2019},
}

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» Christoph Pomrehn
» Daniel Klein
» Andreas Kolb
» Peter Kaul
» Rainer Herpers