This contribution investigates the application of established pansharpening algorithms for the fusion of hyperspectral images from Raman microspectroscopy and panchromatic images from conventional brightfield microscopy. Seven different methods based on multiresolution analysis and component substitution were applied and evaluated through visual assessment and quantitative quality measures at full and reduced resolution. The results indicate that, among the considered concepts, multiresolution methods are the more promising approaches for a physically and chemically meaningful fusion of the considered modalities. Here, pansharpening based on high-pass filtering led to the best results.
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.
