Road Extraction from Lidar Data Using
Support Vector Machine Classification
Ali Akbar Matkan, Mohammad Hajeb, and Saeed Sadeghian
Abstract
This paper presents a method for road extraction from lidar
data based on
SVM
classification. The lidar data are used
exclusively to evaluate the potential in the road extraction
process. First, the
SVM
algorithm is used to classify the lidar
data into five classes: road, tree, building, grassland, and
cement. Then, some misclassified pixels in the road class
is removed using the road values in the normalized Digital
Surface Model and Normalized Difference Distance fea-
tures. In the postprocessing stage, a method based on Radon
transform and Spline interpolation is employed to auto-
matically locate and fill the gaps in the road network. The
experimental results show that the proposed algorithm for
gap filling works well on straight roads. The proposed road
extraction algorithm is tested on three datasets. An accuracy
assessment indicated 63.7 percent, 60.26 percent and 66.71
percent quality for three datasets. Finally, centerline of the
detected roads is extracted using mathematical morphology.
Introduction
Road information plays an important role in many modern
applications, including transportation, automatic navigation
systems, traffic management, and crisis management, and
enables existing geographic information system (
GIS
) data-
bases to be updated more efficiently. In the past two decades,
automatic road extraction has become an important topic in
remote sensing, photogrammetry, and computer vision. In
addition, recent advances in lidar systems and their enor-
mous potential in automatic feature extraction motivate the
development of automatic road extraction algorithms based
on lidar data.
Many studies have been performed on road extraction
from remotely sensed data. Mena (2003) provided a bibli-
ography of nearly 250 references related to this topic. Hu
(2003) proposed a method for road extraction from lidar data.
In this approach, specified range and intensity thresholds
were used in an exponential membership function. Alharty
and Bethel (2003) successfully extracted roads from lidar
data using some constraints proportional to the road prop-
erties such as intensity and proximity to a digital terrain
model (
DTM
). Zhu
et al
. (2004) extracted city road by use of
digital image and laser data. Height and edge of high objects
were obtained from laser data and road edges were detected
from a digital image. Shadowed parts were reconstructed
by a spline-approximation algorithm. Hu
et al
. (2004) used
high-resolution imagery combined with lidar data for road
extraction. They used an iterative Hough transform algorithm
to distinguish car parks from roads stripes. Clode
et al
. (2005)
presented a road classification technique for lidar data based
on region growing. Akel
et al
. (2005) suggested a method to
extract roads from lidar data using a segmentation technique.
Clode
et al
. (2007) used a hierarchical classification tech-
nique to progressively classify the lidar points into road or
non-road groups. The resultant binary classification was then
vectorized by convolving a Phase Coded Disk (PCD). Youn
et al
. (2008) utilize lidar data and true orthoimage for urban
road extraction in sequential steps. First, the candidate road
pixels were selected from the true orthoimage based on a free
passage measure that is called the “acupuncture” method.
Then, a first-last return analysis and morphological filter were
used with the lidar data to mask building pixels. Supervised
classification techniques were used with the lidar intensity
and true orthoimage to mask grass pixels. In (Li
et al
., 2008) a
method based on a parallel algorithm was proposed for road
extraction from lidar data. Harvey and McKeown (2008) suc-
cessfully extracted roads using both lidar and multi-spectral
source data. Choi
et al
. (2008) proposed a method to extract
urban roads using range and intensity lidar data combined
with clustered road point information and the global ge-
ometry of the road system. Tiwari
et al
. (2009) proposed an
integrated approach to road extraction using lidar and high-
resolution satellite data. An object-oriented fuzzy rule-based
algorithm identifies roads based on high resolution satellite
images, and then a complete road network is extracted from
a combination of lidar and high-resolution satellite data. In
(Zhu and Mordohai, 2009) the lidar data are segmented based
on both edge and region properties and these two features
are combined to obtain a heat map of road likelihood using
hypothesis testing. A minimum cover algorithm is then used
to find a set of road segments which best cover this likelihood
map. Samadzadegan
et al
. (2009) proposed a method based on
a multiple classifier system (
MCS
) to extract roads from lidar
data. Gong
et al
. (2010) extracted roads from lidar data using
k-mean clustering method and refined the results using spec-
tral information from aerial images. Zhang (2010) presented
a method to identify road regions and road edges using lidar
data. The road segments and road edge points were detected
according to a local extreme-signal detection filter accord-
ing to elevation data whit a prior knowledge of the minimal
with of roads. Wang
et al
. (2011) applied lidar data fused
with aerial images to extract 3
D
road information by use of
Ali Akbar Matkan and Mohammad Hajeb are with the Remote
Sensing and GIS Department, Shahid Beheshti University,
Evin, Tehran, Iran (
).
Saeed Sadeghian is with the Geomatics College of the Nation-
al Cartographic Center, National Cartographic Center, Meraj
Street, Azadi Square, Tehran, Iran.
Photogrammetric Engineering & Remote Sensing
Vol. 80, No. 5, May 2014, pp. 409–422.
0099-1112/14/8005–409
© 2014 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.80.5.409
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
May 2014
409
