| dc.contributor.author | Bridgelall, Raj | |
| dc.contributor.author | Rafert, James B. | |
| dc.contributor.author | Tolliver, Denver D. | |
| dc.contributor.author | Lee, EunSu | |
| dc.description.abstract | Traffic carrying flammable, corrosive, poisonous, and radioactive materials continues to increase
in proportion with the growth in their production and consumption. The sustained risk of
accidental releases of such hazardous materials poses serious threats to public safety. The early
detection of spills will potentially save lives, protect the environment, and thwart the need for
expensive clean up campaigns. Ground patrols and terrestrial sensing equipment cannot scale
cost-effectively to cover the entire transportation network. Remote sensing with existing airborne
and spaceborne platforms has the capacity to monitor vast areas regularly but often lack the
spatial resolution necessary for high accuracy detections. The emergence of unmanned aircraft
systems with lightweight hyperspectral image sensors enables a resolution agile approach that
can adapt both spatial and spectral resolutions in real-time. Equipment operators can exploit such
a capability to enhance the resolution of potential target materials detected within a larger fieldof-
view to verify their identification or to perform further inspections. However, the complexity
of algorithms available to classify hyperspectral scenes limits the potential for real-time target
detection to support rapid decision-making. This research introduces and benchmarks the
performance of a low-complexity method of hyperspectral image classification. The hybrid
supervised-unsupervised technique approaches the performance of prevailing methods that are at
least 30-fold more computationally complex. | en_US |
| dc.rights | In copyright. Permission to make this version available has been granted by the author and publisher. | |
| dc.title | Resolution Agile Remote Sensing for Detection of Hazardous Material Spills | 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 | 2017-11-30T22:31:45Z | |
| dc.date.available | 2017-11-30T22:31:45Z | |
| dc.date.issued | 2016 | |
| dc.identifier.uri | https://hdl.handle.net/10365/26900 | |
| dc.subject.lcsh | Transportation. | en_US |
| dc.subject.lcsh | Remote sensing. | en_US |
| dc.subject.lcsh | Hazardous substances -- Transportation. | en_US |
| dc.identifier.orcid | 0000-0003-3743-6652 | |
| dc.description.sponsorship | University Transportation Centre | en_US |
| dc.description.sponsorship | U.S. Department of Transportation (USDOT) | en_US |
| dc.description.sponsorship | Mountain Plains Consortium (MPC) | 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 | 10.3141/2547-08 | |
