EXTRACTING ROAD CENTERLINES FROM HIGH
RESOLUTION REMOTE SENSING IMAGES BY ACTIVE
WINDOW LINE SEGMENT MATCHING
Y. Yang1, C.Q. Zhu2, D. Zhang1
1 - Xian Research Institute of Surveying and Mapping, Department of Cartography and
GIS Engineering, Xian, China
2 - Zhengzhou Institute of Surveying and Mapping, Zhengzhou, China
yytoall@126.com
Abstract: A semi-automatic method based on active window line segment matching is
proposed for extracting road centerlines from high spatial resolution remote sensing
images. Road centerlines can be tracked automatically by defining a series of active
template windows lying on a road centerline which start from a user-given input point, and
then by using thresholding, line segment matching and improved SSDA. Meanwhile, a
little manual processing is allowed in case there is something wrong with automatically
road extraction. Experiments on 0.61m-resolution QuickBird and 1m-resolution IKONOS
satellite images demonstrate that main road networks can be extracted rapidly and
accurately by using the proposed approach. Moreover this extraction is robust to noise
interference.
Key words: high resolution remote sensing images; road extraction; active window; line
segment matching
1 Introduction
With the development of sensors technology, more and more high-resolution satellite
images such as QuickBird and IKONOS images are used in a wide range of applications,
thus facilitating information extraction from the remote sensing images. Road extraction is
very important for data update in GIS, image matching, target detection and digital
automatic mapping [1]. However, satisfactory results have not been achieved in terms of
accuracy and integrity by using existing automatic road extraction techniques
[2-8]
.
Post-processing by human being is usually inevitable. Therefore, it is more practical to
research semi-automatic methods with a little human being interaction for quick and
accurate road extraction [9-12].
In this paper, a new semi-automatic method based on active window line segment
matching is proposed for extracting road centerlines from high-resolution remote sensing
images. It can be adaptive to the variations in brightness, and is robust to noise interference
on the two sides of road centerlines. In addition, the searching strategy of sequential
similarity detection algorithm (SSDA) is improved. As a result, faster matching can be
obtained. Due to the combination of automatic tracking with little human interaction, this
method is more practical.
2 Model and Methodology
2.1 Road Feature Analysis
Fig. 1 shows some examples of main roads in 0.61m-resolution QuickBird imagery.
We can notice that road centerlines are clearly visible. They can be regarded as linear
features which have distinctive brightness patterns compared to their surroundings.
Moreover, most roads vary slowly without sharp changes in their directions. Therefore, the
road centerlines can be approximately considered as curves which consist of many short
straight lines connected. The extraction of road centerlines can naturally be transformed
into the extraction of short straight lines. Based on the this idea, a new method, which is
referred to as active window line segment matching, is developed for extracting road
centerlines from high-resolution remote sensing images.
(a) Example 1
(b) Example 2
(c) Example 3
(d) Example 4
Fig.1 Examples of roads in 0.61m-resolution QuickBird imagery
2.2 Active Window Line Segment Matching Method
The basic idea of active window line segment matching is as follows:
2.2.1 Defining a template window
At first, select a point in the road centerline as a reference central point to define a
template window, as shown in figure 2(a). Then, a thresholding operation is performed to
transform the image into a binary one within the window.
The thresholding operation is represented as:
 0,
G (i, j )  
255,
if
 0,
G (i, j )  
255,
if
G (i, j )  T ,
otherwise
if a centerline is darker than its surroundings；
G (i, j )  T ,
otherwise
if a centerline is brighter than its surroundings.
where T is the threshold which is calculated by using the optimum threshold algorithm
presented in [13].
After the above operation, the road centerline within the window will become a black
line whose intensity is zero, as shown in figure 2(b). Then the black line obtained can be
used for the following line segment matching.
(a) Original image
(b) Thresholding result
Fig.2 Original image and thresholding result
2.2.2 Line segment matching
By translating and rotating the template window in a prescribed scope, we can get
some different target windows. The optimum matching is achieved when obtaining
maximum similarity between line segment features from target window and template
window. The principle of line segment matching is illustrated in figure 3.
The similarity transform from template window to target window can be expressed as
follows.
 xt a r etg   c o s
y
  
 t a r etg   s i n
 s i n  xt e m p la t e s1 


c o s  yt e m p l a t e s 2 
where ( xtemplate, ytemplate) is the coordinates of a point within a template window,
( xt arg et , yt arg et ) a point within a target window,  the rotation angle between the target
and template windows, and ( s1 , s2 ) the respective shift in x and y directions.
Target window

Template window
s2
Line segment feature
s1
Fig.3 Line segment matching
The similarity measure in line segment matching is defined as

iM
jN
 w | f
i  M j  N
j
ij
 g ij |
where f ij and g ij are intensity values at point (i, j ) within the template and target
windows, respectively. w j is a weighting factor to determine relative importance of
matching points, which reaches its largest value 1 along the axis of the line segment and
decreases gradually with the increase of distance from the axis. The size of each window is
(2M  1)  (2M  1) and line width for matching is (2 N  1) . In a matching, the smaller the
value of  , the more similar in line segment feature between the template and target
windows.
2.2.3 Searching optimum target window based on SSDA
In order to find the target window whose line segment feature is most similar to the
template window, the template window is first moved in the road direction to create an
initial target window. Then the initial target window is rotated and translated in a
prescribed scope, and their line segment features are matched to find out the most similar
window. In this operation, SSDA searching strategy is used for the sake of its fast matching
speed.
The basic idea of SSDA is that if the accumulated error in the computation of
similarity is greater than a prescribed threshold, the computation is terminated, thereby
significantly reducing the amount of computation for non-matching points and enhancing
the matching speed. In this algorithm, it is difficult to find out a suitable threshold. SSDA
can be improved by ignoring the threshold, and matching is performed in the way of
self-study. At first, the accumulated error of all pixels within the initial target window is
calculated. From the second target window, the calculation of the accumulated error stops
when it reaches or surpasses the minimum one occurred before. In this way, the optimum
target window corresponding to the smallest accumulated error can be obtained once a
Posteriority
thorough searching is completed. If the target window corresponding
to the smallest
accumulated error is found in the earlier stage, the subsequent amount of calculation will
be very small. For this reason, we designed a priority order for the translation and rotation
of the initial target window, as shown in figure 4, to find the optimum target window as
soon as possible.
3
3
3
3
3
3
2
2
2
3
3
2
1
2
3
3
2
2
2
3
3
3
3
3
3
1- Initial target center
Priority
Initial target window direction
Posteriority
Fig.4 Priority order of translation and rotation for initial target window
3 Road Network Extraction Scenario
Figure 5 shows the procedure of our road centerline extraction method. Firstly, a
starting point and a directional point on a road centerline are inputted by a user to create a
template window. To ensure that line segment matching is performed along the road
centerline, it is necessary to rectify these two points to become respective midpoints of the
road centerline.
Secondly, an initial target window can be obtained by moving the template window
along the road direction. Thresholding is applied to the template and the target window,
and line segment matching is performed.
Then, a series of target windows can be obtained by rotating and translating the initial
window, and line segment matching is performed based on SSDA. Once the matching is
successfully completed, a new road center and a new road direction are available.
Figure 6 shows the process of continuous active window matching. If the minimum
value of  is greater than a prescribed value E, the matching fails. In this case, manual
processing is necessary, which includes proceeding with tracking, moving backwards or
crossing road sections. As long as manual processing is completed, the matching will
continue as before until complete road information is obtained.
Input starting point and directional point
Central point correction
Template and initial target window creation
Line segment matching based on SSDA
的线匹配
New road center and orientation obtained
Thresholding
Yes
Successful matching？
Fig.5 The procedure of road centerline extraction
Fig.6 Continuous active window matching
4 Experimental Results
In this section, we used the proposed method to extract road networks in remote
sensing images of Shenzhen city. These images were obtained from the satellite QuickBird
whose resolution is 0.61m. Some parameters are set as follows: the size of template and
target windows is 25  25 pixels, the distance between template and initial target window is
20 pixels, the translation limit of initial target window is  2  2 pixels, the range of
rotation angle is from -45º to 45º, the line width in matching is 5 pixels. Once a starting
point and a directional point are inputted on a road centerline, the line segment matching
will be completed automatically, thus obtaining a series of central points along the road.
Figure 7 shows the extraction results of the roads in figure 1. Figure 8 shows the extraction
results of another long and curved road. Automatic matching fails when a road centerline is
lost or shaded. In this case, manual processing is necessary. After that, automatic matching
will be restored.
(a) Result 1
(b) Result 2
(c) Result 3
(d) Result 4
Fig.7 Results of extracted road centerlines in fig.1
Fig.8 Another extracted result of a curved road
Another road extraction experiment is applied to a remote sensing image of part of
Shenzhen city. The image is obtained from the satellite IKONOS whose resolution is 1
meter. The area is 12 km  9 km, and the parameters in the experiment remain unchanged.
The final experiment results are shown in figure 9. The total length of extracted road
networks is 310 kilometers, and time required in the experiment performed in a 3 GHz
Pentium(R) 4 computer is 60 minutes, including manual processing.
Fig.9 Results of road network extraction in Shenzhen
5 Conclusions
From above experiments, main road networks can be extracted accurately and quickly
by using the proposed method. The road extraction in 1m-resolution images can be dealt
with nearly in real time, which is 3 times faster than that performed in 0.61m-resolution
images. However, in both cases, they are much faster than manual operation. Meanwhile,
this extraction is robust to noise interference. Matching is only performed between line
segment features within windows of interest, excluding the outside of the centerline, which
can overcome the effect of noise outside the centerline. The windows in the matching are
continuously updated and the threshold is obtained from the current window. This can
make the matching adapt the variations in the intensity along road centerlines. In addition,
it makes this method more practical to combine the automatic tracking with little manual
processing.
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