15arspc Submission 4

8/8/2019 15arspc Submission 4

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EXTRACTION OF COMPLEX ROAD JUNCTIONS FROM AERIALIMAGERY

M. Ravanbakhsh, C.S. Fraser

Cooperative Research Center for Spatial InformationDepartment of Geomatics

University of Melbourne VIC 3010, AustraliaPhone number: +613 8344 9186

Fax number: +613 9349 5185[m.ravanbakhsh, c.fraser]@unimelb.edu.au

Abstract

Road junctions are important components of a road network. However, they areusually not explicitly modelled in existing road extraction approaches. In thisresearch, complex road junctions, as a class of road junctions, are modelled indetail as area objects and a new approach for their automatic extraction isproposed. A complex road junction includes a varying number of traffic islands.Therefore, a detailed model of complex road junctions needs to regard theexistence of these features. A new snake-based approach that makes use of the ziplock snake idea and whose external force is the combination of theGradient Vector Flow force and the Balloon force is proposed. The Balloonforce is applied based on the geometric shape of the road junction. The existingroad arms provide fixed boundary conditions for the proposed snake.

Furthermore, a level set framework is proposed for the extraction of trafficislands. The approach has been tested using black-and-white aerial images of 0.1 m ground resolution, taken over suburban and rural areas. The resultsobtained demonstrate the validity of the proposed approach.

Introduction

Geospatial databases contain various man-made objects among which roadsare of special importance. Road junctions are in turn important components of aroad network. However, they are usually not explicitly modelled in existing roadextraction approaches. Road junctions in road network extraction systems havemainly been modelled as point objects at which three or more road segments

meet (Gerke, 2006; Zhang, 2003; Wiedemann, 2002). In contrast, in Gautamaet al. (2004), Laptev et al. (2000) and Mayer et al. (1998), junctions are treatedas planar objects. This kind of modelling does not always reflect the requireddegree of detail obtainable in high resolution aerial images (Fig. 1a). A moredetailed modelling of road junctions is necessary for data acquisition at largescales.

Laptev et al. (2000) and Mayer et al. (1998) have both employed a snake modelto delineate junctions, and Gautama et al. (2004) have used a differential ridgedetector in combination with a region growing operator to detect junctions.

However, none of the described approaches have attempted to model trafficislands, which are often present in the central area of junctions. Traffic islands



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are important componenets in traffic management and car navigation systems.In particular, future car navigation systems will require detailed and accuratetopographic information (Brenner, 2008). Therefore, traffic islands should beincluded in any detailed topographic database. Since complex junctions, as a

class of road junctions, contain islands in their centre, a detailed junction modelneeds to consider the existence of small islands.

A complex road junction can contain several small islands located in its centralarea. The number of these varies in different junctions depending on thenumber of crossing roads and the functionality of the junction, and islands maybe of diverse geometrical shape. Furthermore, they may be partially occludedby shadows from traffic lights, traffic signs, vehicles and trees. These propertiesimply that the extraction of islands is a challenging problem within aerial imageanalysis.

In this paper, an attempt is made to model complex road junctions in rural and

suburban areas and an approach for their automatic extraction is proposed. The junction outline is extracted using a proposed snake-based approach. Snakes,also called parametric active contours (Kass et al., 1988), are especially usefulfor delineating objects that are hard to model with rigid geometric primitives.Since junction borders are of diverse shapes, including various degrees of curvature, snakes are well suited for this task. As a supplementary operation,traffic islands are captured using a level set method.

The proposed approach uses existing road arms as input, leading to theextraction of refined complex junctions. In the following section, a model for complex junctions is first introduced. Various steps of the proposed strategy are

then illustrated in Sect. 3. In Sect. 4, results from the implementation of theproposed approach using aerial imagery of 0.1 m ground resolution arepresented and discussed, together with an evaluation of their quality. The paper concludes with a summary and outlook.

Road Junction Model

The conceptual model of a complex junction is depicted and described in Fig.1b. According to the model, a road junction is composed of two parts: the road

junction itself and the road arms. The junction, where the arms are connected,is composed of a border and its central area where traffic islands are located. Aroad arm is composed of one or more road segments, and is defined in terms of geometry and radiometry as follows:

• Geometry: A road arm is a rectilinear object which is represented as a ribbonwith a constant width and two parallel road edges.

• Radiometry: A road arm is considered to be a homogeneous region, highlycontrasted against its surroundings. The absolute brightness depends uponthe road surface material.

Disturbances such as occlusions and shadows are not explicitly included in themodel.



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(a) (b)

Figure 1. (a) Superimposition of vector data on a high resolution aerial image of acomplex road junction; (b) complex road junction model

Extraction Approach

The proposed strategy consists of two steps (Fig. 2a). First, road junctions arereconstructed by means of a snake-based method. Second, traffic islands aredelineated using level sets. The proposed approach has the aerial image andthe road arms as input, and the borders of the junction area as output. Thereader is referred to Ravanbakhsh et al. (2008) or Ravanbakhsh (2008) for adescription of how the road arms are extracted.

Road Junction Reconstruction In this section, the snake model used to delineate the junction outline is brieflydescribed to provide a basis for further discussion. Additional details areprovided in Ravanbakhsh et al. (2008) and Ravanbakhsh (2008). Snakes, alsocalled parametric active contours (Kass et al., 1988), are useful techniques for delineating objects that are hard to model with rigid geometric primitives.Snakes are well suited to modelling road junctions since the borders are of diverse shape with various degrees of curvature. Snakes are polygonal curvesassociated with an objective function that combines an image term (externalenergy) and measurement of the image force (e.g. the edge strength). There isalso a regularization term (internal energy) and a minimization of the tensionand curvature of the polygon. The curve is deformed so as to iterativelyoptimize the objective function. Traditional snakes are sensitive to noise andneed precise initialization. Since junction borders have various degrees of curvature, a close initialization cannot often be provided. As a result, traditionalsnakes can easily get stuck in an undesirable local minimum.

To overcome these limitations, the ziplock snake model was developed(Neuenschwander et al., 1997). A ziplock snake consists of two parts: an activepart and a passive part. The active part is further divided into two segments,indicated as head and tail, respectively (Fig. 2b). The active and passive partsof the ziplock snake are separated by moving force boundaries. Unlike theprocedure for a traditional snake, the external force derived from the image isturned on only for the active parts. Thus, the movement of passive vertices isnot affected by any image forces. Starting from two short pieces, the active partis iteratively optimized to image features, and the force boundaries areprogressively moved towards the centre of the snake. Each time that the force



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boundaries are moved, the passive part is re-interpolated using the position anddirection of the end vertices of the two active segments. Optimization is stoppedwhen force boundaries meet each other.

Ziplock snakes need far less initialization effort and are less affected by theshrinking effect from the internal energy term. Furthermore, their computation ismore robust because the active part, whose energy is minimized, is alwaysquite close to the contour being extracted. This modified snake model candetect image features even when the initialisation is far away from the solution.However, it can still become confused in the presence of disturbances. In highresolution aerial images, such disturbances may destabilize the ziplock’s activevertices. As a result convergence may not occur or the snake may get trappednear the initial position. As a means of overcoming this problem, an externalforce with a large capture range can be applied.

The Gradient Vector Flow (GVF) field (Xu & Prince, 1997), which is an example

for such an external force, is used in the proposed approach. The GVF field wasaimed at addressing two issues: a poor convergence to concave regions, andproblems associated with the initialisation. It is computed as a spatial diffusionof the gradient of an edge map derived from the image. This computationcauses diffuse forces to exist far from the object, and crisp force vectors to benear the edges. The GVF field points toward the object boundary when verynear to the boundary, but varies smoothly over homogeneous image regions,extending to the image border. The main advantage of the GVF field is that itcan capture a snake from a long range. Thus, the problem of far initializationcan be alleviated.

The Evolution of a ziplock snake is illustrated in Fig. 2b. The snake is fixed atthe head and tail, and it consists of two parts, the active and the passivevertices. These parts are separated by moving force boundaries. The activeparts of the snake consist of the head and tail segments.

Force Boundaries

Passive VertexActive Vertex

Head

Tail

(a) (b)

Figure 2. (a) Workflow of complex road junction extraction; (b) evolution of a ziplocksnake.

The GVF is defined to be the vector field )),(),,((),( y xv y xu y xG = that minimizes

the energy functional:



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dxdy f G f vvuu E y x y x222222 ||||)( ∇−∇++++=∫∫ μ (1)

where f ∇ is the vector field computed from ),( y x f having vectors pointing

toward the edges. ),( y x f is derived from the image and it has the property thatit is larger near the image edges.

The regularization parameter μ should be set according to the amount of noisepresent in the image; more noise requires a higher value of μ . Through use of calculus of variations (Courant & Hilbert, 1953), the GVF can be found bysolving the following Euler equations:

0))((

0))((222

222

=+−−∇

=+−−∇

y x y

y x x

f f f vv

f f f uu

μ

μ (2)

where 2∇ is the Laplacian operator and x f and y f are partial derivatives of f

with respect to x and y .

Let ))(),(()( s ys xsV = be a parametric active contour in which s is the curvelength and x and y are the image coordinates of the 2D-curve. The internalsnake energy is then defined as

[ ]22int |)(|)(|)(|)(

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))(( sV ssV ssV E sss β α += (3)

wheresV and ssV are the first and second derivatives of V with respect to s . The

functions )(s and )(s β control the elasticity and the rigidity of the contour,respectively. The global energy

))(())((int sV E sV E E img+= (4)

needs to be minimized, with α α =)(s and β β =)(s being constants. Minimizationof the energy function of Eq. 4 gives rise to the following Euler equations:

0)(

))(()()( =

∂∂

++−sV

sV E sV sV

imgssssss β α (5)

where )(sV stands for either )(s x or )(s y , and ssV and ssssV denote the secondand fourth derivatives of V , respectively. After approximation of the derivativeswith finite differences, and conversion to vector notation with ),( iii y xV = , theEuler equations take the form



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[ ][ ] [ ]

1 1 1 1 2 1

1 1 1 1 2

( ) ( ) 2

2 2 2 ( , ) 0

i i i i i i i i i i

i i i i i i i i

V V V V V V V

V V V V V V G u v

α α β

β β

− + + − − −

− + + + +

− − − + − +− − + + − + + = (6)

where ),( vuG is the GVF vector field . Eq. 6 can be written in matrix form as

( , ) 0KV G u v+ = (7)

where K is a pentadiagonal matrix.

Finally, the motion of the GVF ziplock snake can be written in the form (Kass etal., 1988)

[ ] [ ][ ]1

11( ) ( G(u,v) | )t

t t

vV K I V γ γ κ −

−−= + ∗ −(8)

where γ stands for the viscosity factor (step size) determining the rate of convergence and t is the iteration index. κ alters the strength of the externalforce.

It is noteworthy that the proposed model still might fail to detect the correctboundaries in the following cases:

• High variation of curvature at the junction border resulting in an initializationthat is too poor in some parts, with the consequence that the snakesbecomes and remain straight.

• The junction central area lacks sufficient contrast with the surroundings,causing the curve to converge to nearby features.

Through the use of shape description parameters such as curvature computedfrom the snake vertices, another force can be added to the GVF force field. Thisis the so-called balloon force, which lets the contour have a more dynamicbehaviour (Cohen, 1991), thereby addressing the two described problems. Thisnew force, which makes the contour act like a balloon, applies an inflating effectto the contour to localize the concave part of the junction outline:

)(1 snk F = (9)

where )(sn is the normal unitary vector of the curve at point )(sV and 1k is theamplitude of the force. The combination of the GVF force field and the balloonforce modifies Eq. 8 to the form

[ ] [ ][ ]1

111( ) ( ( , ) | ( ))t

t t v v

V K I V G u v k n sγ γ κ −−−= + ∗ − −

r

(10)

The balloon force is activated when the snake’s passive and active parts areapproximately straight, i.e. their overall curvature, which is defined as the sum

of the absolute curvatures along the curve, is below a threshold. It is applied



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only on the passive part of the curve. This is regarded as lying outside the junction’s border, whereas the snake at the active parts is assumed to be on theright track. The direction in which the balloon force is applied is towards the

junction central area. Fig. 3 illustrates an example of snake evolution and shows

intermediate results.

(a) (b) (c) (d)Figure 3. Delineation of a complex junction’s outline; (a) Initial snakes in black and

road arms in white; (b) & (d) snakes during evolution; (d) junction outline.

Traffic Island Extraction As discussed in the previous section, snakes are suited for delineating objects,such as traffic islands, that are difficult to model with rigid geometric primitives.Snakes are represented as explicit parametric contours. As a result, they do notallow for automatic changes of topology. Thus, the simultaneous extraction of an a priori unknown number of objects, which require such a change of topologyduring the extraction process, is not straightforward. Several approaches havebeen proposed to address this problem. McInerney and Terzopoulos (1995) andSzeliski et al. (1993) proposed heuristic procedures for detecting possiblesplitting and merging of the initial contour. In contrast, level sets (Osher andSethian, 1988) allow for splitting and merging in a natural way and are thusmore suited to solve the changing topology problem.The basic idea of level sets is both to represent contours as the zero level set of an implicit function in a higher dimension, usually referred as the level setfunction φ, and to evolve the level set function according to a partial differentialequation (PDE). It is well known that a signed distance function, a functionwhich introduces the minimum distance from every point in a defined domain tothe zero isocontour of a level set function, must satisfy the desirable property of |∇φ |=1 (Osher and Fedkiw, 2002). The following formula has been proposed toprovide the internal energy of a snake which penalizes the deviation of φ via asigned distance function (Li et al., 2005):

dxdyP 2

)1||(

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)( −∇= ∫ Ω φ φ (11)

where )(φ P is a metric to characterize how close the function φ is to a signeddistance function in a specified computational domain 2 R⊂Ω . The externalenergy is defined by

)()()( φ φ λ φ ggm Av L E += (12)



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where λ>0 and v is a constant. The length term )(φ g L and area term )(φ g A aredefined by

∫ Ω ∇= ||)()( dxdyg Lg φ φ δ φ (13)

∫ Ω −=

)()( dxdygH Ag φ φ (14)

where δ is the univariate Dirac function, H the Heaviside function, and g theedge indicator function defined by

2|*|11

I Gg

σ ∇+= (15)

Where σ G is the Gaussian kernel with standard deviation σ and I an image.The following total energy function can now be defined:

)()()( φ φ φ m E P E += (16)

where μ>0 controls the balance between the first and the second term. Bycalculus of variation, and application of the steepest descent process for minimization of the energy functional equation, the evolution equation of the

level set function is obtained (Li et al., 2005):

)(vg)||

)div(g()]||

div([t

φ δ φ φ

φ λδ φ φ

φ μ φ +

∇

∇+∇

∇−Δ=∂∂

(17)

In order to focus the extraction of traffic islands within the proper image regions,we make use of the delineated junction outline as prior information.

First, the image area in which islands are located is clipped from the image. Thesearch space for islands is further restricted to an area around the estimated

junction centre point called the island area (Fig. 4a). To begin the islandextraction by curve evolution, the initial level set function needs to beconstructed. It is computed within the island area. Prior segmentation of thisarea is carried out to derive a rough idea of island regions from which the initiallevel set function is constructed.

It is assumed that most of the junction area shows a rather homogeneous greyvalue distribution, as would be expected in a suburban or rural road network.This assumption is equivalent to expecting that there are not too manydisturbances such as cars or shadows in the junction. Under thesecircumstances, the histogram of the pre-procesed image shows one main peak,which is related to the surface material of the road, and potentially a few smaller peaks related to the islands and various disturbances. To start pre-processing,



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morphological opening is applied in order to remove distortions such as roadmarkings. Subsequently, closing with the same structuring element is performedto eliminate small shadows, etc. Next, Gaussian smoothing is applied to theimage (Fig. 4b) followed by thresholding the intensity histogram (Fig. 4c). The

threshold value is computed by applying the Iterative Self-Organizing DataAnalysis Technique Algorithm (ISODATA) (Ridler & Calvard, 1978) on thehistogram of the island area. At this stage, convex areas inside the junction areconsidered to be potential islands.

The initial level set function is constructed from the segmented image so thatareas in white are assigned a negative value and black areas take a positivevalue of the same magnitude. The zero level curve of the initial level setfunction is shown in Fig. 4d. The initial level set function then evolves accordingto the evolution equation (Eq. 17) as shown in Figs. 4e & f.

In order to select the final islands, geometric and topological constraints are

introduced based on the properties of islands, because, in addition to theislands, some undesirable features such as vehicles and large shadow areasmay have been extracted as island candidates. Small closed areas below acertain size are removed (Fig. 4g). Since island candidates must be locatedwithin the junction outline, those curves that touch the junction outline are alsoremoved. Finally, islands possess boundaries with a small curvature variation,so the contours with high curvature variations, i.e. their mean curvature isgreater than a certain threshold, are eliminated.

(a) (b) (c) (d)

(e) (f) (g)

Figure 4. Sequence for the extraction of traffic islands; (a) Island area; (b) pre-processed image; (c) segmented image; (d) the zero level curve of the correspondinginitial level set function; (e) intermediate result of the zero level curve evolution withλ=4, μ=0.13, v=1.5 after 50 iterations; (f) the zero level curve of the final level setfunction after 265 iterations; (g) closed curves are retained as island candidates. Twocars covering apparent areas of 13 and 11 m² are eliminated.

Result and Evaluation

Experiments were conducted using 0.1m GSD black-and-white aerial



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orthoimagery obtained with the Digital Mapping Camera (DMC). The imagerycovered rural and suburban areas. The Authoritative Topographic CartographicInformation System of Germany (ATKIS) was used as the source of externalvector data. The content of ATKIS is approximately equivalent to topographic

maps of 1:25000 scale. In this database, roads are modelled as linear objects.The proposed approach was tested on eight complex road junction samples, of which some results are shown in Fig. 5 to highlight its capabilities. The snakeapproach can deal with a variety of disturbances caused by trees, shadows androad markings, as can be seen in the Figs. 5a, d, e examples in the figure.

The proposed level sets approach proved to be capable of delineating trafficislands as most were successfully extracted. Nevertheless, two islands were notextracted, one in the Fig. 5b,c examples. There were two reasons for this:

• The first island was very narrow. As a result, morphological operationsapplied in the segmentation step, caused a decrease in size of the island andconsequently the narrow parts of the island were almost washed out.

• Poor contrast between the island surface and the surrounding area causedanother island to be nearly radiometrically washed out during pre-processing.Thus, it could not be detected in the following steps.

In order to evaluate the performance of the junction extraction approach, theextracted junctions were compared to the manually plotted junctions used asreference data. The comparison was carried out by matching the extracted roadborders resulting from the connection of a junction border to its associated roadarms to the reference data using the so-called buffer method (Heipke et al.1998). Although the buffer width can be defined using the required accuracy of ATKIS, which for a road object is defined as 3m, it was decided to set the buffer width within the range of 0.5 m to 3 m, i.e. 5 pixels to 30 pixels, in concert withthe image resolution of 0.1 m. This allowed assessment of the relevance of theapproach for practical applications that demand varying degrees of accuracy.An extracted road border is assumed to be correct if the maximum distancebetween the border and its corresponding reference does not exceed the buffer width. Furthermore, a reference road border is assumed to be matched if themaximum deviation from the extracted object is within the buffer width. Basedon these assumptions, the following quality measures were both proposed andadopted:

• Completeness: the ratio of the number of matched reference road borders tothe number of reference objects.

• Correctness: the ratio of the number of correctly extracted road borders tothe number of extracted objects.

• Geometric accuracy: the average distance between the correctly extractedroad border and the corresponding reference border, which is expressed as aRoot Mean Square (RMS) value falling within the range of [0, buffer width].

Table 1a shows the evaluation results computed with different buffer widthvalues. As the buffer width value increased from 0.5m to 3m, so thecompleteness of the road border extraction also increased, implying that the



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results are more complete for higher buffer width values. Increases in geometricaccuracy are inversely proportional to the buffer width value so that resultsobtained with a value of 0.5m are more accurate than those obtained with alarger buffer width. For the buffer width value of 0.5 m, the completeness is

rather low. The reason is that a slight deviation of the extraction results from thetrue boundaries, which exceeds the buffer width, frequently occurs due todisturbances and sometimes also due to road markings. Table 1b shows theevaluation result of the island extraction. The buffer width was set to 0.3 m and0.5 m.

Table 1. Evaluation results for road borders (a) and for traffic islands (b)

(a) (b)

Buffer width(m) 0.5 1 2 3 Buffer width (m) 0.3 0.5

Referencenumber of road

borders26 26 26 26 Reference

number of islands 17 17

Completeness 54% 65% 76% 81% Completeness 65% 71%

Correctness 60% 87%Geometricaccuracy (m) 0.31 0.41 0.54 0.59

Geometricaccuracy (m) 0.18 0.22

Likely disturbances, which deteriorate the geometrical accuracy of the

detection, are of two kinds for the islands: first, tree shadows situated inside theislands (Fig. 5e), and second, vehicles and traffic signs beside the islands andtheir shadows. The adopted method of level sets cannot overcome thesedisturbances.

Summary and Outlook

A proposed new approach to automatic extraction of complex road junctionshas been described and analysed. It comprises a snake-based approach for thedelineation of the junction outline and a level set approach for detection of trafficislands.

The GVF external force field was integrated into the traditional ziplock snake toincrease the snake’s capture range, and the balloon force was added toovercome both the curvature variation along the junction border and poor results on the border. The proposed snake model can largely overcome variouskinds of disturbances.

Level sets were primarily chosen because of their adaptive topology. While thepresented results indicate some problems with the developed method, they alsodemonstrate the feasibility of extracting traffic islands from black and white highresolution aerial images using level sets, since 15 out of 17 islands wereextracted, and the correctness of the extracted areas amounted to 87%.

Nevertheless, partial occlusion of islands by shadows as well as poor contrast



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on the island boundaries (Fig. 5c) cannot be overcome by the proposed methodat this stage.

There are several possibilities to further enhance the results obtained so far for the island extraction process, and to be able to deal with more complex scenes.The incorporation of high-level prior knowledge about the shape of trafficislands within the level set framework could provide a solution to theseproblems. Furthermore, such shape-driven level set schemes potentially reducethe number of detected island candidates that are obtained at the evolutionstage.

(a) (b) (c) (d) (e) (f)

Figure 5. Sample complex junction extraction results for scenes with varying degrees of complexity including disturbances.

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