Extracting Pavement Surface Distress Conditions Based on High Spatial Resolution Multispectral Digital Aerial Photography
Digital Object Identifier (DOI)
State transportation agencies regularly collect data on pave-ment surface distresses. These data are used to assess overall pavement conditions and to make maintenance and repair de-cisions. Routinely-acquired and publically-available high spa-tial resolution (HSR) multispectral digital aerial photography provides a potential method for collecting distress information that can supplement or replace currently-used technologies. Principal component analysis and linear least squares regres-sion models were used to evaluate the potential of using HSR multispectral digital aerial photographs to estimate pavement surface overall distress conditions. Various models were de-veloped using HSR multispectral digital aerial photographs of different spatial resolution (6-inch, 12-inch, and 24-inch) and reference pavement surface distress data collected manually at multiple sample sites using standard protocols. The results show that the spectral response of HSR multispectral digital aerial photographs correlate strongly with reference distress rates at all tested spatial resolutions, but the 6-inch aerial photos exhibit the strongest correlation (R2 > 0.95), even when using only half of the sample sites (R2 > 0.92). These results in-dicate that straightforward analysis of HSR multispectral digital aerial photographs, routinely acquired by most municipalities and states, can permit assessment of pavement surface distress conditions as well as current manual evaluation protocols.
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Citation / Publisher Attribution
Photogrammetric Engineering & Remote Sensing, v. 81, issue 9, p. 709-720
Scholar Commons Citation
Zhang, Su; Bogus, Susan M.; Lippitt, Christopher D.; Neville, Paul R. H.; Zhang, Guohui; Chen, Cong; and Valentin, Vanessa, "Extracting Pavement Surface Distress Conditions Based on High Spatial Resolution Multispectral Digital Aerial Photography" (2015). CUTR Faculty and Staff Publications. 81.