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By measuring the spectral reflection from the four different road conditions dry, wet, icy, and snowy asphalt, a method of classification for the different surfaces-using two and three wavelengths-is developed. The method is tested against measurements to ascertain the probability of wrong classification between the surfaces. From the angular spectral response, the fact that asphalt and snow are diffuse reflectors and water and ice are reflective are confirmed.

In this paper, we present a hyper-spectral imaging system and practical calibration procedure using a low-cost calibration reference made of polytetrafluoroethylene. The imaging system includes a hyperspectral camera and an active source of illumination with a variable spectral distribution of intensity. The calibration reference is used to measure the relative reflectance of any material surface independent of the spectral distribution of light and camera sensitivity. Winter road conditions are taken as a test application, and several spectral images of snow, icy asphalt, dry asphalt, and wet asphalt were made at different exposure times using different illumination spectra. Graphs showing measured relative reflectance for different road conditions support the conclusion that measurements are independent of illumination. Principal component analysis of the acquired spectral data for road conditions shows well separated data clusters, demonstrating the system’s suitability for material classification.

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