A File Structure and Reference Data Set for High Resolution Hyperspectral 3D Point Clouds

Abstract

Hyperspectral imaging has been extensively studied in remote sensing. In this community, several approaches exist for classifying different organic and an-organic materials. However, this data is usually collected from large distances (flight or satellite data) and hence lacks geometric precision, which is required for robotic applications like mapping and navigation. In this paper, we present a reference data set that maps hyperspectral intensity data to a terrestrial 3D laser scanner to generate what we call hyperspectral point clouds (HPCs). To organize and distribute the resulting massive data, we designed an HDF5 file structure that is the basis to feed information derived from the raw data into robot control frameworks like ROS.

Publication
Proceedings of the IFAC Symposium on Intelligent Autonomous Vehicles. IFAC Symposium on Intelligent Autonomous Vehicles 2019, July 3-5 Gdansk Poland IFAC 2019.
Date
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