Pre-print Manuscript of Article: 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. Raj Bridgelall, Ph.D. Page 1/15 Hyperspectral imaging utility for transportation systems Raj Bridgelall 1 *, J. Bruce Rafert 2 , and Denver Tolliver 3 North Dakota State University, Upper Great Plains Transportation Institute, P.O. Box 863676, Plano, TX 75086 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. Keywords: Highway capacity, hyperspectral remote sensing, pushbrooming, spectroscopy, transportation, unmanned. 1. INTRODUCTION The global transportation system is interdependent on the performance of large and complex multimodal and intermodal facilities that move people, goods, and waste. The U.S. transportation infrastructure consists of more than 4 million miles of roadways, 600,000 bridges, 1.5 million miles of above- and under-ground oil and gas pipelines, 100,000 miles of railroad tracks, 25,000 miles of navigable waterways, and 19,000 airports 1 . Trucks alone carry more than 8 billion tons of goods valued at more than $10 trillion each year 2 . All countries rely on a high-performance multi-modal transportation infrastructure for sustained economic growth and prosperity. Consequently, the need for regular performance measures of the entire network has become critical. Climatic factors and heavy vehicle traffic accelerate deterioration. Figure 1 illustrates the interdependencies between economic growth and performance measures. Population growth and quality-of-life pursuits drive economic growth, which in turn fuels higher demand to transport more people and goods. To meet those demands while realizing economies of scale, organizations increase the size of the carriers such as trucks, rail cars, ships, pipelines, and aircrafts. Consequently, the infrastructure must bear a higher load density from the aggregate increase in the gross weight of carriers and their miles traveled. Together with climatic factors, an increase in load density accelerates the deterioration 1 Corresponding Author: Program Director, Center for Surface Mobility Applications & Real-Time Simulation environments (SMARTSe SM ), Upper Great Plains Transportation Institute, North Dakota State University, P.O. Box 863676, Plano, TX 75086, Phone: 408-607-3214, E-mail: raj@bridgelall.com 2 Professor, Department of Physics, North Dakota State University, 1536 Cole Boulevard, Suite 140, Lakewood, CO 80401, Phone: 1- 720-238-0080, E-mail: bruce.rafert@ndsu.edu 3 Director, Upper Great Plains Transportation Institute, North Dakota State University, P.O. Box 6050, Fargo, ND 58108, Phone 1- 701-231-7190, E-mail: denver.tolliver@ndsu.edu