Dataset of the botanical garden in Osnabrück.
In this section we describe the main features of the generated reference data set. It consists of 16 hyperspectral laser scans and covers an area of approximates 70 × 75,m. An aerial photo of the scanned area and the rough layout of the scan positions and covered area is shown in Fig. 4. Each 3D laser scan was taken with a field of view of 360 × 100° with an horizontal and vertical angular resolution of 0.05°, resulting in approximately 70 million points per scan. For each scan position, we collected the full spectral data. Aerial view of a part of the Botanical Garden at the University of Osnabrück with an indication of the scanned area (Image provided by the City of Osnabrück, geo.osnabrueck.de). 150 buckets between 400 nm and 1000 nm. Additionally, we took 5 24 megapixel RGB images per scan for RGB annotation. The total amount of collected raw data sums up to 45 GB for this relatively small area. The single scans were automatically registered based on the GPS pose estimations provided by the Riegl laser scanner and the robots odometry using slam6d of the 3DTK Toolkit. The header image shows exemplary 3D views on the data set with different modalities. The top row shows renderings of the same scenes with annotated spectral intensities at wavelengths of 400, 600 and 800 nm respectively. For rendering, the measured intensities were normalized and mapped to an blue to red color gradient, visualizing the different intensity distributions at different wavelengths. The pictures in the bottom row show overview renderings of the whole data set with RGB annotation (left) and mapped NDVI values. Human-made an-organic structures like pathways are clearly distinguishable in this representation. We added this picture to demonstrate the potential of the combination of hyperspectral and spatial information for robotic applications. Especially for semantic classification the use of such sensor combinations seem to be extremely beneficial, especially in combination with methods from remote sensing, where classification of hyperspectral images is an established discipline.
Further information is published in: A File Structure and Reference Data Set for High Resolution Hyperspectral 3D Point Clouds.
all_scans_spectral.h5 ~49.4 GB
user@pc:~$ wget http://kos.informatik.uni-osnabrueck.de/3Dscans/all_scans_spectral.h5