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Runway Assessment via Remote Sensing

Dabbiru, L., Wei, P., Harsh, A., White, J., Ball, J. E., Aanstoos, J.V., Doyle, J., Jackson, S., & Newman, J. (2015). Runway Assessment via Remote Sensing. 2015 IEEE Applied Imagery Pattern Recognition Workshop (AIPR). Cosmos Club, Washington, DC: IEEE. 1-5.

Abstract

Airport pavements are constructed to provide adequate support for the loads and traffic volume imposed by aircrafts. One aspect of pavement evaluation is the pavement condition which is determined by the types and extent of distresses. These include cracking, rutting, weathering, and others that may affect pavement surface roughness and the potential for FOD (Foreign Object Debris). Pavement evaluations are necessary to assess the ability to safely operate aircraft on an airfield. The purpose of this study is to explore the potential use of microwave remote sensing to assess the pavement surface roughness. Radar backscatter responds to surface roughness as well as dielectric constant. The resulting changes in backscatter can convey information about the degree of cracking and surface roughness of the runway. In this study, we develop a relation between the Terrain Ruggedness Index (TRI) of the runway and radar backscatter magnitudes. Radar data from the TerraSAR-X satellite is used, along with airborne LiDAR data (30 cm spacing). Modest linear correlation was found between the vertical co-polarization channel of the radar data and TRI values computed in 5 by 5 pixel windows from the LiDAR elevation data. Over four different test areas on the runway, the coefficients of determination ranged from 0.12 to 0.46.


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