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Authors: Christian Winkens ; Florian Sattler and Dietrich Paulus

Affiliation: University of Koblenz-Landau, Germany

Keyword(s): Hyperspectral Imaging, Terrain Classification, Spectral Analysis, Autonomous Robots.

Related Ontology Subjects/Areas/Topics: Applications ; Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Pattern Recognition ; Robotics ; Segmentation and Grouping ; Software Engineering

Abstract: 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 multi-class classification is evaluated against a novel hyperspectral ground truth dataset specifically created for this purpose.

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Paper citation in several formats:
Winkens, C.; Sattler, F. and Paulus, D. (2017). Hyperspectral Terrain Classification for Ground Vehicles. In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP; ISBN 978-989-758-226-4; ISSN 2184-4321, SciTePress, pages 417-424. DOI: 10.5220/0006275404170424

@conference{visapp17,
author={Christian Winkens. and Florian Sattler. and Dietrich Paulus.},
title={Hyperspectral Terrain Classification for Ground Vehicles},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP},
year={2017},
pages={417-424},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006275404170424},
isbn={978-989-758-226-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2017) - Volume 5: VISAPP
TI - Hyperspectral Terrain Classification for Ground Vehicles
SN - 978-989-758-226-4
IS - 2184-4321
AU - Winkens, C.
AU - Sattler, F.
AU - Paulus, D.
PY - 2017
SP - 417
EP - 424
DO - 10.5220/0006275404170424
PB - SciTePress

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