Hyperspectral Terrain Classification For Ground Vehicles

Hyperspectral imaging increases the amount of information incorporated per pixel in comparison to normal RGB color cameras. Conventional spectral cameras as used in satellite imaging use spatial or spectral scanning during acquisition which is only suitable for static scenes. In dynamic scenarios, such as in autonomous driving applications, the acquisition of the entire hyperspectral cube at the same time is mandatory. We investigate the eligibility of novel snapshot hyperspectral cameras. It captures an entire hyperspectral cube without requiring moving parts or line-scanning. The sensor is tested in a driving scenario in rough terrain with dynamic scenes. Captured hyperspectral data is used for terrain classification utilizing machine learning techniques. The multiclass classification is evaluated against a novel hyperspectral ground truth dataset specifically created for this purpose.