Extracting Pavement Surface Distress Conditions
Based on High Spatial Resolution
Multispectral Digital Aerial Photography
Su Zhang, Susan M. Bogus, Christopher D. Lippitt, Paul R.H. Neville, Guohui Zhang, Cong Chen, and Vanessa Valentin
Abstract
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 (R
2
> 0.95), even when
using only half of the sample sites (R
2
> 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.
Introduction
The serviceability of road networks primarily relies on pave-
ment conditions, and subsequently, federal, state, and local
transportation agencies dedicate a large amount of time and
money to routinely evaluate pavement conditions as part of
their management programs. Pavement surface distress data
are collected and used by these agencies to determine the
serviceability of individual roads, and then to make decisions
on the distribution of limited resources for maintenance and
construction projects.
Currently, most state and local transportation agencies use
either manual evaluation or automated evaluation to collect
data solely for the purpose of pavement surface evaluation at
significant expense (McGhee, 2004). We therefore explore the
utility of routinely-acquired and publically-available high spa-
tial resolution (
HSR
) visible range digital aerial photography to
supplement or replace dedicated surveys of pavement surface
condition. Many counties and municipalities routinely ac-
quire
HSR
multispectral digital aerial photos, and most make
these images freely available to the public. These photos cover
all ground features including roadways, meaning they contain
information that may permit discrimination of pavement sur-
face distress. Modern aerial photographs are in digital format,
which means they can be readily shared with partner agencies
and analyzed to produce standardized results through image
processing techniques. The availability of these images offers
the potential of using routinely-collected and publically-
available data for standardized evaluation of pavement surface
distress, reducing the evaluation cost and time while improv-
ing the comparability of results.
This paper explores the utility of routinely-acquired and
publically-available
HSR
multispectral digital aerial pho-
tography for the evaluation of overall pavement surface
distress. Specifically, the intent of this study is to examine
how well overall pavement surface distress can be estimated
from
HSR
multispectral digital aerial photography. Principal
components analysis (
PCA
) and linear least squares regres-
sion models were used to evaluate the potential of using
HSR
multispectral digital aerial photographs to infer pavement
surface distress.
Background
Pavement surface distress information is essential to pave-
ment management. Pavement management activities and deci-
sions at all levels (i.e., federal, state, and local) are supported
by pavement surface condition information of varying detail
(Haas
et al.,
1994). Pavement evaluation can lead to effective
allocation of limited resources for timely maintenance and
repair (Haas
et al.,
1994; Hudson and Uddin, 1987). Pavement
evaluation is also necessary to measure the effectiveness of
various maintenance techniques and repair methods (Hudson
et al.,
1987; Hudson and Uddin, 1987).
To characterize the conditions of existing pavements,
surveys are conducted to assess one or more of four criteria:
roughness, distress, structural capacity, and friction (Gram-
ling, 1994). Pavement distress and roughness are the basic ele-
ments typically included in quantification of the overall pave-
ment condition, although structural capacity and friction may
also be incorporated (Gramling, 1994; Prakash
et al.,
1994).
Current Pavement Surface Distress Evaluation Methods
Currently, two types of pavement surface distress evalua-
tion methods have been broadly adopted by state and local
Su Zhang, Susan M. Bogus, Guohui Zhang, Cong Chen,
and Vanessa Valentin are with the Department of Civil
Engineering, University of New Mexico, Albuquerque, NM
87131-0001 (
).
Christopher D. Lippitt is with the Department of Geography
and Environmental Studies, University of New Mexico,
Albuquerque, NM 87131-0001.
Paul R.H. Neville is with the Earth Data Analysis Center,
University of New Mexico, Albuquerque, NM 87131-0001.
Photogrammetric Engineering & Remote Sensing
Vol. 81, No. 9, September 2015, pp. 709–720.
0099-1112/15/709–720
© 2015 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.81.9.709
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
September 2015
709
