| dc.contributor.author | Bridgelall, Raj | |
| dc.contributor.author | Rafert, J. Bruce | |
| dc.contributor.author | Tolliver, Denver D. | |
| dc.contributor.author | Atwood, Don | |
| dc.description.abstract | Transportation agencies expend significant resources to inspect critical infrastructure such as roadways, railways, and
pipelines. Regular inspections identify important defects and generate data to forecast maintenance needs. However,
cost and practical limitations prevent the scaling of current inspection methods beyond relatively small portions of the
network. Consequently, existing approaches fail to discover many high-risk defect formations. Remote sensing
techniques offer the potential for more rapid and extensive non-destructive evaluations of the multimodal
transportation infrastructure. However, optical occlusions and limitations in the spatial resolution of typical airborne
and spaceborne platforms limit their applicability. This research proposes hyperspectral image classification to isolate
transportation infrastructure targets for high-resolution photogrammetric analysis. A plenoptic swarm of unmanned
aircraft systems will capture images with centimeter-scale spatial resolution, large swaths, and polarization diversity.
The light field solution will incorporate structure-from-motion techniques to reconstruct three-dimensional details of
the isolated targets from sequences of two-dimensional images. A comparative analysis of existing low-power wireless
communications standards suggests an application dependent tradeoff in selecting the best-suited link to coordinate
swarming operations. This study further produced a taxonomy of specific roadway and railway defects, distress
symptoms, and other anomalies that the proposed plenoptic swarm sensing system would identify and characterize to
estimate risk levels. | en_US |
| dc.rights | In copyright. Permission to make this version available has been granted by the author and publisher. | |
| dc.title | Hyperspectral Range Imaging for Transportation Systems Evaluation | 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 | 2017-12-06T14:39:22Z | |
| dc.date.available | 2017-12-06T14:39:22Z | |
| dc.date.issued | 2016 | |
| dc.identifier.uri | https://hdl.handle.net/10365/26995 | |
| dc.subject.lcsh | Transportation. | en_US |
| dc.subject.lcsh | Hyperspectral imaging. | en_US |
| dc.subject.lcsh | Intelligent transportation systems. | en_US |
| dc.subject.lcsh | Photogrammetry. | en_US |
| dc.identifier.orcid | 0000-0003-3743-6652 | |
| dc.identifier.citation | Bridgelall, R., Rafert, J. B., Atwood, D., Tolliver, D., "Hyperspectral Range Imaging for Transportation Systems Evaluation," in Proc. SPIE Smart Structures/NDE 2016, Las Vegas, NV, March 24, 2016. | 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.college | College of Science and Mathematics | |
| ndsu.department | Transportation and Logistics | |
| ndsu.department | Physics | |
