Field Calibration of Hyperspectral Cameras for Terrain Inference

Abstract

Intra-class terrain differences such as water content directly influence a vehicle’s ability to traverse terrain, yet RGB vision systems may fail to distinguish these properties. We argue that expanding the evaluation of a terrain’s spectral content beyond red-green-blue channels to the near infrared spectrum provides useful information for such intra-class identification and route planning. However, accurate analysis of this spectral information is highly dependent on ambient illumination. We demonstrate a system architecture to collect and register multi-wavelength, hyperspectral images from a mobile robot and describe an approach to reflectance calibrate cameras under varying illumination conditions. To showcase the practical applications of our system, HYPER DRIVE, we demonstrate the ability to calculate vegetative health indices and soil moisture content from data collected from an off-road mobile robot with greater consistency than at-imager radiance.