| dc.contributor.author | Bridgelall, Raj | |
| dc.contributor.author | Rafert, J. Bruce | |
| dc.contributor.author | Tolliver, Denver D. | |
| dc.description.abstract | The global transportation system is massive, open, and dynamic. Existing performance and condition assessments of the
complex interacting networks of roadways, bridges, railroads, pipelines, waterways, airways, and intermodal ports are
expensive. Hyperspectral imaging is an emerging remote sensing technique for the non-destructive evaluation of
multimodal transportation infrastructure. Unlike panchromatic, color, and infrared imaging, each layer of a hyperspectral
image pixel records reflectance intensity from one of dozens or hundreds of relatively narrow wavelength bands that
span a broad range of the electromagnetic spectrum. Hence, every pixel of a hyperspectral scene provides a unique
spectral signature that offers new opportunities for informed decision-making in transportation systems development,
operations, and maintenance. Spaceborne systems capture images of vast areas in a short period but provide lower spatial
resolution than airborne systems. Practitioners use manned aircraft to achieve higher spatial and spectral resolution, but
at the price of custom missions and narrow focus. The rapid size and cost reduction of unmanned aircraft systems
promise a third alternative that offers hybrid benefits at affordable prices by conducting multiple parallel missions. This
research formulates a theoretical framework for a pushbroom type of hyperspectral imaging system on each type of data
acquisition platform. The study then applies the framework to assess the relative potential utility of hyperspectral
imaging for previously proposed remote sensing applications in transportation. The authors also introduce and suggest
new potential applications of hyperspectral imaging in transportation asset management, network performance
evaluation, and risk assessments to enable effective and objective decision- and policy-making. | en_US |
| dc.rights | In copyright. Permission to make this version available has been granted by the author and publisher. | |
| dc.title | Hyperspectral Imaging Utility for Transportation 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 | 2017-12-06T15:13:12Z | |
| dc.date.available | 2017-12-06T15:13:12Z | |
| dc.date.issued | 2015 | |
| dc.identifier.uri | https://hdl.handle.net/10365/26998 | |
| dc.subject.lcsh | Transportation. | en_US |
| dc.subject.lcsh | Hyperspectral imaging. | en_US |
| dc.subject.lcsh | Spectrum analysis. | en_US |
| dc.identifier.orcid | 0000-0003-3743-6652 | |
| dc.identifier.citation | Bridgelall, R., Rafert, J. B., Tolliver, D., "Hyperspectral Imaging Utility for Transportation Systems," Proc. SPIE 9435 Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, 943522, San Diego, CA, March 12, 2015. | 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 | |
