Hyperspectral image classification has received more attention from researchers in recent years. Hyperspectral imaging systems utilize sensors, which acquire data mostly from the visible through the near infrared wavelength ranges and capture tens up to hundreds of spectral bands. Using the detailed spectral information, the possibility of accurately classifying materials is increased. Unfortunately conventional spectral cameras sensors use spatial or spectral scanning during acquisition which is only suitable for static scenes like earth observation. In dynamic scenarios, such as in autonomous driving applications, the acquisition of the entire hyperspectral cube in one step is mandatory. To allow hyperspectral classification and enhance terrain drivability analysis for autonomous driving we investigate the eligibility of novel mosaic-snapshot based hyperspectral cameras. These cameras capture an entire hyperspectral cube without requiring moving parts or line-scanning. The sensor is mounted on a vehicle in a driving scenario in rough terrain with dynamic scenes. The captured hyperspectral data is used for terrain classification utilizing machine learning techniques. A major problem, however, is the presence of shadows in captured scenes, which degrades the classification results. We present and test methods to automatically detect shadows by taking advantage of the near-infrared (NIR) part of spectrum to build shadow maps. By utilizing these shadow maps a classifier may be able to produce better results and avoid misclassifications due to shadows. The approaches are tested on our new hand-labeled hyperspectral dataset, acquired by driving through suburban areas, with several hyperspectral snapshotmosaic cameras.