TY - JOUR U1 - Zeitschriftenartikel, wissenschaftlich - begutachtet (reviewed) A1 - Pomrehn, Christoph A1 - Klein, Daniel A1 - Kolb, Andreas A1 - Kaul, Peter A1 - Herpers, Rainer T1 - Supervised classification of monomodal and multimodal hyperspectral data in vibrational microspectroscopy: A comprehensive comparison JF - Chemometrics and Intelligent Laboratory Systems N2 - 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. KW - Multimodal hyperspectral data KW - Pattern recognition KW - Chemical imaging KW - Vibrational microspectroscopy KW - Supervised classification Y1 - 2019 UN - https://nbn-resolving.org/urn:nbn:de:hbz:1044-opus-43308 SN - 0169-7439 SS - 0169-7439 U6 - https://doi.org/10.1016/j.chemolab.2018.11.013 DO - https://doi.org/10.1016/j.chemolab.2018.11.013 VL - 184 SP - 112 EP - 122 PB - Elsevier ER -