| dc.contributor.author | Bridgelall, Raj | |
| dc.contributor.author | Rafert, J. Bruce | |
| dc.contributor.author | Tolliver, Denver D. | |
| dc.contributor.author | Lee, EunSu | |
| dc.description.abstract | The emergence of lightweight full-frame hyperspectral cameras is destined to enable
autonomous search vehicles in the air, on the ground, and in water. Self-contained and
long-endurance systems will yield important new applications, for example, in emergency
response and the timely identification of environmental hazards. One missing capability is
rapid classification of hyperspectral scenes so that search vehicles can immediately take
actions to verify potential targets. Onsite verifications minimize false positives and
preclude the expense of repeat missions. Verifications will require enhanced image quality,
which is achievable by either moving closer to the potential target or by adjusting the
optical system. Such a solution, however, is currently impractical for small mobile
platforms with finite energy sources. Rapid classifications with current methods demand
large computing capacity that will quickly deplete the on-board battery or fuel. To develop
the missing capability, the authors propose a low-complexity hyperspectral image classifier
that approaches the performance of prevalent classifiers. This research determines that the
new method will require at least 19-fold less computing capacity than the prevalent
classifier. To assess relative performances, the authors developed a benchmark that
compares a statistic of library endmember separability in their respective feature spaces. | en_US |
| dc.rights | In copyright. Permission to make this version available has been granted by the author and publisher. | |
| dc.title | Rapid Hyperspectral Image Classification to Enable Autonomous Search Systems | en_US |
| dc.type | Article | en_US |
| dc.type | Preprint | en_US |
| dc.description | Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM). | en_US |
| dc.date.accessioned | 2017-11-27T19:23:02Z | |
| dc.date.available | 2017-11-27T19:23:02Z | |
| dc.date.issued | 2016 | |
| dc.identifier.uri | https://hdl.handle.net/10365/26877 | |
| dc.subject.lcsh | Transportation. | en_US |
| dc.subject.lcsh | Autonomous vehicles. | en_US |
| dc.subject.lcsh | Hyperspectral imaging. | en_US |
| dc.identifier.orcid | 0000-0003-3743-6652 | |
| dc.description.sponsorship | U.S. Department of Transportation (USDOT) (Grant DTRT13-G-UTC38) | en_US |
| dc.description.uri | https://www.ugpti.org/about/staff/viewbio.php?id=79 | |
| dc.language.iso | en_US | en_US |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.contributor.organization | Upper Great Plains Transportation Institute | |
| ndsu.college | College of Business | |
| ndsu.department | Transportation and Logistics | |
| ndsu.doi | 10.1255/JSI.2016.A5 | |
