@InProceedings{7362481,
  author = {R. Bridgelall and J. B. Rafert and D. Tolliver},
  booktitle = {2015 23rd European Signal Processing Conference (EUSIPCO)},
  title = {Hyperspectral applications in the global transportation infrastructure},
  year = {2015},
  pages = {739-743},
  abstract = {Hyperspectral remote sensing is an emerging field with potential applications in the observation, management, and maintenance of the global transportation infrastructure. This study introduces a general analytical framework to link transportation systems analysis and hyperspectral analysis. The authors introduce a range of applications that would benefit from the capabilities of hyperspectral remote sensing. They selected three critical but unrelated applications and identified both the spatial and spectral information of their key operational characteristics to demonstrate the hyperspectral utility. The specific scenario studies exemplifies the general approach of utilizing the outputs of hyperspectral analysis to improve models that practitioners currently use to analyze a variety of transportation problems including roadway congestion forecasting, railway condition monitoring, and pipeline risk management.},
  keywords = {geophysical image processing;railways;transportation;hyperspectral utility;roadway congestion forecasting;railway condition monitoring;pipeline risk management;transportation system analysis;hyperspectral remote sensing;global transportation infrastructure;Hyperspectral imaging;Vehicles;Analytical models;Resistance;Hyperspectral image processing;intelligent transportation systems;remote sensing;smart infrastructure;unmanned aircraft systems},
  doi = {10.1109/EUSIPCO.2015.7362481},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2015/papers/1570092793.pdf},
}

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This study introduces a general analytical framework to link transportation systems analysis and hyperspectral analysis. The authors introduce a range of applications that would benefit from the capabilities of hyperspectral remote sensing. They selected three critical but unrelated applications and identified both the spatial and spectral information of their key operational characteristics to demonstrate the hyperspectral utility. 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