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Title: Imagery classification for autonomous ground vehicle mobility in cold weather environments
Authors: Olivier, Jason L.
Shoop, Sally A. (Sally Annette)
Keywords: Terrain
Publisher: Cold Regions Research and Engineering Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: Miscellaneous Paper (Engineer Research and Development Center (U.S.)) ; no. ERDC/CRREL MP-21-25
Is Version Of: Olivier, Jacob and Sally Shoop "Imagery Classification for Autonomous Ground Vehicle Mobility in Cold Weather Environments" Proceedings of the ISTVS 20th International and 9th Americas Conference. 2021.
Abstract: Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.
Description: Miscellaneous Paper
Gov't Doc #: ERDC/CRREL MP-21-25
Rights: Approved for Public Release; Distribution is Unlimited
Size: 17 pages / 2.27 MB
Types of Materials: PDF/A
Appears in Collections:Miscellaneous Paper

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