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.
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.
Navigation of Unmanned Ground Vehicles (UGV) in unknown environments is an active area of research for mobile robotics. A main hindering factor for UGV navigation is the limited range of the on-board sensors that process only restricted areas of the environment at a time. In addition, most existing approaches process sensor information under the assumption of a static environment. This restrains the exploration capability of the UGV especially in time-critical applications such as search and rescue. The cooperation with an Unmanned Aerial Vehicle (UAV) can provide the UGV with an extended perspective of the environment which enables a better-suited path planning solution that can be adjusted on demand. In this work, we propose a UAV-UGV cooperative path planning approach for dynamic environments by performing semantic segmentation on images acquired from the UAV's view via a deep neural network. The approach is evaluated in a car park scenario, with the goal of providing a path plan to an empty parking space for a ground-based vehicle. The experiments were performed on a created dataset of real-world car park images located in Croatia and Germany, in addition to images from a simulated environment. The segmentation results demonstrate the viability of the proposed approach in producing maps of the dynamic environment on demand and accordingly generating path plans for ground-based vehicles.
