automated extraction of road network from medium-and high-resolution images

ISSN 1054-6618, Pattern Recognition and Image Analysis, 2006, Vol. 16, No. 2, pp. 239–248. © Pleiades Publishing, Inc., 2006.

Automated Extraction of Road Networkfrom Medium- and High-Resolution Images

1

A. P. Dal Poz, R. B. Zanin, and G. M. do Vale

São Paulo State University, Department of Cartography, Rua Roberto Simonsen, 305, 19060-900 Presidente Prudente, SP, Brazil

e-mail: {aluir, zanin, gmvale}@pnidente.unesp.br

Abstract

—This paper presents an automatic methodology for road network extraction from medium- and high-resolution aerial images. It is based on two steps. In the first step, the road seeds (i.e., road segments) areextracted using a set of four road objects and another set of connection rules among road objects. Each roadobject is a local representation of an approximately straight road fragment and its construction is based on acombination of polygons describing all relevant image edges, according to some rules embodying road knowl-edge. Each road seed is composed by a sequence of connected road objects in which each sequence of this typecan be geometrically structured as a chain of contiguous quadrilaterals. In the second step, two strategies forroad completion are applied in order to generate the complete road network. The first strategy is based on twobasic perceptual grouping rules, i.e., proximity and collinearity rules, which allow the sequential reconstructionof gaps between every pair of disconnected road segments. This strategy does not allow the reconstruction ofroad crossings, but it allows the extraction of road centerlines from the contiguous quadrilaterals representingconnected road segments. The second strategy for road completion aims at reconstructing road crossings.Firstly, the road centerlines are used to find reference points for road crossings, which are their approximatepositions. Then these points are used to extract polygons representing the contours of road crossings. This paperpresents the proposed methodology and experimental results.

DOI:

10.1134/S1054661806020118

Received January 26, 2006

1

1. INTRODUCTION

Road extraction is of fundamental importance in thecontext of spatial data capturing and updating for GIS(Geographic Information Systems) applications. Sub-stantial work on road extraction has been accomplishedsince the 1970s in computer vision and digital photo-grammetry, with pioneering works by, e.g., Bajcsy andTavakoli (1976) and Quam (1978). At times, the use ofthe term “extraction” is vague, invoking various mean-ings among the diverse image analysis community. Inthis context, the task of road extraction is related to twosubtasks: recognition and delineation. By convention, aroad extraction algorithm is categorized according tothe extent to which it addresses either subtask, therebyimplying the relative level of automation (Doucette etal. 2001). Usually, road extraction methods which inprinciple do not need human interaction are categorisedas automatic, and those requiring human interaction assemi-automatic. Thus, automatic methods address bothroad extraction subtasks, and semi-automatic methodsaddress only the geometric delineation of the roads,leaving the high-level decisions (i.e. recognition) to ahuman operator who uses his natural skill to set themeaning “road” to the object.

1

The text was submitted by the authors in English.

With regard to semi-automatic methods, probablysome existing ones can already be incorporated intooperational workflows. Semi-automatic approachesmay be divided into two broad categories. The firstincludes road-following approaches, in which the roadis sequentially traced by using only local road informa-tion (McKeown and Denlinger 1988, Vosselman and deKnecht 1995, Dal Poz and Silva 2002, Kim et al. 2004).These approaches are usually initialized by two closeseed points on the road, one being a starting point andthe other a point to define the road’s direction. The sec-ond category includes active contour models (Kass etal. 1987, Neuenschwander et al. 1997, Grüen and Li1997, Agouris et al. 2000), piecewise parabola fittingusing image constraint (Hu et al. 2004), and dynamicprogramming optimisation (Merlet and Zerubia 1996,Grüen and Li 1997, Dal Poz and Vale 2003), in whichsome type of simultaneous curve fitting is used. Usu-ally, these approaches are initialized by a few seedpoints roughly describing the road.

At present, fully automated methods for road extrac-tion seem to be far from a mature state and, conse-quently, no such operational system is expected to beavailable in the near future. Fully automated methodsattempt to completely circumvent human interventionduring the extraction process. Recently, differentapproaches have been proposed. Some automatedmethods apply a skilful integration of contextual infor-mation and a priori knowledge to the road extractiontask. A sophisticated example is found in Baumgartner

APPLICATIONPROBLEMS

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et al. (1999), in which different resolutions, grouping,and context are used to extract road networks fromhigh-resolution images. An improvement of thisapproach using the so-called “ziplock” snake for bridg-ing gaps is reported in Laptev et al. (2000). In order todeal with the high complexity of urban scene, Hinz(2004) integrated detailed road knowledge and roadcontext by explicitly formulating a scale-dependentmodel. Wessel (2004) also used contextual informationfor supporting road network extraction from SARImagery. Other automated approaches based on somekind of optimization techniques have been proposedrecently. Youn et al. (2004) assume that the road net-work and block pattern in the city have a semi-regulargrid pattern. Based on this assumption, the image issegmented according to dominant road directions. Thenthe road segments are detected and refined by applyingan adaptive snakes method. Stoica et al. (2004) proposea method to extract roads in remote sensed imagesbased on stochastic geometry and reversible jumpMonte Carlo Markov Chains dynamic. The road net-work is approximated by connected line segments,resulting in a probabilistic model to be solved by theMaximum a posteriori (MAP) estimation. Song andCivco (2004) use Support Vector Machine (SVM),which also is a optimization technique. Firstly, SVM isused to classify the input image into road and non-roadimages. Next, the road image is segmented into geo-metrically homogeneous objects using a region grow-ing technique. Finally, a thresholding process is per-formed to extract road features, which are further pro-cessed by thinning and vectorization to obtain roadcenterlines. Nowadays, the tendency for research inautomated road extraction is to use new sensor data toconfront the difficult task of road extraction based onlyon panchromatic satellite and aerial images. Clode et al.(2004) use both height and intensity information fromLIDAR for extracting roads. Firstly, a DTM is gener-ated from LIDAR data. Basically, the road is classifiedby checking the valid intensity LIDAR range andheight differences from the DTM. Zhu et al. (2004) pro-pose a road extraction technique that combines infor-mation from LIDAR data and aerial photos. Firstly, themethod detects in LIDAR data road edges shadowed bysurrounding high objects. Secondly, digital images areanalysed where road edges are detected. Finally, shad-owed parts are reconstructed by a spline-approximationalgorithm. Hu et al. (2004) also combine informationfrom LIDAR data and aerial photos, taking advantageof deriving multiple clues and constraints to signifi-cantly minimize the uncertainty in the extraction pro-cess. An example of using multispectral images in theproblem of automated road extraction is found in Gaoand Wu (2004). Firstly, an unsupervised classificationis applied to four multispectral bands of Ikonos imageto obtain a binary image with road and non-road pixels.A spatial reasoning-based method is applied to auto-matically extract the road from the previously gener-ated binary image. Finally, recognising that fully auto-

mated system for road network extraction needs poste-rior participation of an operator to complete theextracted road network, Hinz and Wiedemann (2004)show that results attached with confidence values canincrease the efficiency of automatic extraction systemsfor practical applications. This is why the operator, withthe confidence values in hand, can speed up the time-and cost-intensive inspection.

This paper presents an automated methodology forroad network extraction from medium- and high-reso-lution images, in which road seeds are firstly extracted,followed by a completion strategy based on some basicperceptual grouping rules. This methodology is pre-sented in Section 2. Experimental results are presentedand discussed in Section 3. Conclusions are provided inSection 4.

2. METHODOLOGY FOR AUTOMATIC EXTRACTION OF THE ROAD NETWORK

The proposed methodology for automatic road net-work extraction is based on two sequential phases,namely: road seed extraction and road network comple-tion. In the road seed extraction phase, the whole areaof interest is tested for local road property and road seg-ments (i.e., road seeds) are found. Usually, the localroad properties tested are geometric (e.g.: roads aresmooth) and radiometric (e.g.: roads are usually lighterthan the background) in sense. The results of this stepare the road seeds, or a fragmented road network. Thesecond phase, i.e., the road network completion,accomplishes the linking among road seeds and thereconstruction of road crossings. The linking of roadseeds is based on two basic perceptual grouping rules,the proximity and collinearity rules, which allow thesequential reconstruction of gaps between every pair ofdisconnected road segments. In order to reconstructroad crossings, firstly reference points describing theapproximate positions of road crossings are computed.These points are defined as the intersection among roadcenterlines extracted from the connected road seg-ments. The reference points are used for searching edgepoints all around, allowing the organisation of polygonsdelineating road crossings.

2.1. Extraction of Road Seeds

We propose a methodology for road seed extractionwhich is based on a set of four road objects and anotherset of connection rules among them. Each road objectis a local representation of an approximately straightroad fragment and its building is based on a combina-tion of polygons describing all relevant image edges.The polygons representing image edges are extractedusing well-known image processing algorithms such asthe Canny edge detection method followed by an edge-linking algorithm and the split and merge algorithm(Parker 1997). After polygon extraction, some steps areneeded to build road objects. Firstly, two polygons that


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AUTOMATED EXTRACTION OF ROAD NETWORK 241

are candidates for representing the edges of the sameroad are selected. Now, taking one polygon as refer-ence, the algorithm tries to combine their straight linesegments to those of the other polygon. Two straightline segments of different polygons are accepted toform a road object when (1) only two of their endpointsare orthogonally projected onto an opposite straightline segment, whose results are shown as circles inFig. 1, (2) they are close to parallel and the distancebetween them is compatible with the road width(Fig. 1), and (3) the region between them satisfies suchroad knowledge as, for example, road surface is usuallyhomogeneous, road surface has clear contrast with itsadjacent areas, road width does not change much, roadedges are anti-parallel, etc. In other words, as statedbefore, a road object is a representation of an approxi-mately straight road fragment. Moreover, road objectscan be classified in four types, but additional details aregiven in the following sections. The road objects aresequentially connected to each other according to a ruleset, allowing a road seed to be formed (Fig. 1).

In the following, the extraction of road objects andthe way they are combined to construct road seeds aredescribed in detail.

2.1.1. Extraction of road objects.

The road objectsare defined using straight line segments belonging totwo different polygons with characteristics that arecompatible with a road.

Figure 2 shows four road objects found in any roadseed. In the building of a road object, by convention theinferior straight line segment is called the

base

and thesuperior one is called the

candidate

. For each roadobject case, the endpoints of both straight line segments(base and candidate) are orthogonally projected fromone to the other, resulting in only two points projectedbetween endpoints. For example, in Fig. 2a, the end-points of the candidate straight line segment are pro-jected onto two points of the base straight line segment.The opposite occurs with case 2 (Fig. 2b). In relation tocases 3 and 4, as respectively illustrated in Figs. 2c and2d, only one endpoint of a straight line segment is pro-jected between the endpoints of the other straight linesegment, and vice-versa. In all cases, two endpointsbelonging to the base and/or candidate straight line seg-

ment and two projected endpoints are combined tobuild quadrilaterals very similar in shape to rectangles.Each road object gives rise to a quadrilateral—each oneidentified as a crosshatched area in Fig. 2. The axis ofeach quadrilateral coincides with a short road center-line.

The building of the four road objects is based on arule set constructed from a priori road knowledge. Themain rules used to identify and build road objects aredescribed below:

(1)

Anti-parallelism rule:

According to this rule,two image gradient vectors taken at two opposite roadedge points, and belonging to the same road cross sec-tion, are in approximately opposite directions. Besidethis, they are approximately orthogonal to the roadedges. This also means that if the road edges areapproximated by polygons, the image gradient vectors,computed at edge pixels fitted to each straight line seg-ment (of a polygon), are approximately parallel to eachother. Thus, a compact and effective representation forthe image gradient vectors, computed for each straightline segment, is the mean image gradient vector. Thismean vector embodies an important road property (i.e.,anti-parallelism) and its compactness facilitates theimage analysis process for road seed extraction. Themean image gradient vectors for the base and candidatestraight line segments are anti-parallel. By the anti-par-allelism rule, two straight line segments, base and can-didate, are compatible with a road fragment if the ruleis satisfied;

(2)

Parallelism and proximity rule

: by this rule, twostraight line segments, base and candidate, are compat-ible with a road object if they are approximately paral-lel and sufficiently close to each other;

(3)

Homogeneity rule

: the road pixel gray levels donot vary too much, at least within short road segments.Thus, the area inside each quadrilateral must beapproximately homogeneous. Moreover, as a road isusually lighter than the background, the average graylevel inside each quadrilateral should be significantlyhigher than the background. To be accepted as compat-ible, two straight line segments, base and candidate,must satisfy the homogeneity rule;

Road object

Shortest local straight linesegment representing road edges

orthogonal projection ontoopposite polygon

Fig. 1.

Road seed.

(a) (b)

(d)(c)

Case 1 Case 2

Case 3 Case 4

Fig. 2.

Road objects.

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(4)

Contrast rule

: roads usually contrast sharplywith the background. By the contrast rule, two straightline segments, base and candidate, are compatible toform a road object when a high contrast between therespective quadrilateral and its background is verified;

(5)

Superposition rule

: a base and candidate straightline segments are compatible only if two of their end-points can be orthogonally projected onto each other. Itis just this rule that gives rise to the four cases of roadobject depicted in the Fig. 2. For example, in case 1, thetwo endpoints of the candidate straight line segment areorthogonally projected onto the base straight line seg-ment, giving rise to the quadrilateral of road object ofcase 1;

(6)

Fragmentation rule

: as roads are usually smoothcurves, polygons composed of short straight lines arenot usually related to roads. For example, image noisecan generate short and isolated polygons.

However, parts of polygons with very short straightline segments can be extracted from road crossingswhere the curvature is much more accentuated. Anothercase is related to very perturbed (by shadow or obstruc-tion, for example) road edges, which may give rise tomany short straight line segments connected to form apolygon. In these places, road objects are difficult toform, as the two first rules are unlikely to be satisfied.Thus, cases involving short straight line segments arenot considered and possible extraction failures (forexamples, road crossings not extracted) are left to behandled by other strategies, which are based on previ-ously extracted road seeds and other road knowledge,e.g., context (relation between roads and other objectslike trees and buildings).

The order of application of the rules presentedabove is important, mainly when the base and candidatestraight line segments are incompatible. The properorder can avoid, in most cases, the verification of allrules for road object construction. The first rule to beapplied is the sixth as it allows parts or whole polygonspotentially unrelated with road objects to be eliminated.The next rule to be applied is the fifth, avoiding the useof another set of rules in cases when this rule is not sat-isfied. Subsequently, the rules are applied in the follow-

ing order: the second rule, the first rule, the third rule,and then the fourth rule. A road object will be acceptedif all rules are satisfied.

Figure 3 demonstrates how the road seed, shown inFig. 1, is decomposed using the four road objectsdefined above. From right to left, the following casescan be identified: first, second, fourth, and again the lastone. The inverse problem, i.e., the reconstruction of theroad seed using the road objects, is described in the fol-lowing section.

2.1.2. Road segment extraction by grouping roadobjects.

As described before, road objects are con-structed by combining the base and candidate straightline segments, which in turn belong to polygons repre-senting all relevant image edges. Each road object is alocal representation for the longest straight segment ofa road seed. Thus, the problem we have in hand is howto connect the road objects to form road seeds.

Figure 4 shows the possible connections to the leftand to the right between road objects. Figure 4a showsthat the first case road object can connect to the left withthe second and third cases and to the right with the sec-ond and fourth cases. The second case road object(Fig. 4b) can connect to the left with the first and fourthcases and to the left with the first and third cases. Notethat the third and fourth cases (Figs. 4c and 4d, respec-tively) can connect themselves to both the left and right.

In order to construct a road seed by combining roadobjects, two polygons are selected and their straightline segments are combined two-by-two and the result-ing road objects are connected sequentially. The advan-tage of using the connection rules is that the construc-tion of any new road object is limited to one or twocases (Fig. 4). The great problem in polygon combina-tion is the large search space if no heuristic is used. Forhigh-resolution images, an efficient way to drasticallyreduce the search space is to use strategies based on thespace scale, which allow the elimination of most of thepreviously extracted polygons (Baumgartner et al.1999).

Figure 5 shows the combination of polygons repre-senting the edges of the same road. One of the polygonsbeing combined is labeled

base

and the other one is

Fig. 3.

Decomposition of a road seed using the road objects.

2

nd

Case

3

rd

Case

2

nd

Case

2

nd

Case

2

nd

Case

2

nd

Case

3

rd

Case 3

rd

Case

3

rd

Case4

th

Case 4

th

Case

4

th

Case

1

st

Case1

st

Case

1

st

Case 1

st

Case

4

th

Case

4

th

Case3

rd

Case

1

st

Case

(a)

(c) (d)

(b)

Fig. 4.

Connections between road objects.


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labeled

candidate

. As also shown in Fig. 5, the basepolygon can be compatible with more than one candi-date polygon. This means that a base polygon needs tobe combined with candidate polygons until the wholebase polygon is combined. Assuming that every combi-nation gives true results, parts or whole polygons (baseor candidate) are removed from the search space afterthey are combined.

The application of the methodology describedabove to the illustrative example of Fig. 5 would makeit possible to obtain the result shown in Fig. 6.

Supposing that the base polygon is combined fromthe right to the left (Fig. 6a), two road objects are con-structed by combining the base polygon with the firstcandidate polygon. The connection between these tworoad objects generates a first road seed (Fig. 6b), whichin turn can be decomposed into three consecutive quad-rilaterals. The vertices of these quadrilaterals allow thedefinition of a short road seed centerline. Now the com-bination of the base polygon with the second candidatepolygon generates the second road seed constituted byonly one quadrilateral. The straight line segment of thebase polygon not integrating to both road seedsextracted may be a useful piece of information for fur-ther analyses of the reasons for the missing road seed.In any case, it provides evidence that both extractedroad seeds are related. For example, there could be a“T” or “Y” road crossing. Therefore, everything pro-viding information on the road seed extraction problemshould be preserved for further use in strategies for theautomatic completion of the road network.

2.2. Road Network Completion

The completion of the road network aims at recon-structing parts of road network not extracted by the firstphase of the method. Usually, these parts are roadcrossings and other road regions disturbed by, e.g.,trees and shadows, which are incompatible with anyroad object. Thus, there are two types of road networkcompletion to be accomplished, namely: the recon-struction of road crossings and the linking of discon-nected road segments. The linking among road seg-ments is carried out firstly because the resulting con-

nected road segments allow the interpolation of roadcenterlines, which in turn are used to compute theapproximate positions (also known as reference points)for the road crossings. These reference points facilitatethe identification of the extremities of connected roadsegments closer to a road crossing. Now, the problem tobe solved is the extraction of the polygon delineatingthe region enclosed by the road crossing edges and theextremities of the connected road segments.

2.2.1. Linking of road segments.

As described inSection 2.1, a road segment can be viewed as a chain ofconnected quadrilaterals. As a result, the problem ofconnection between two road segments can be solvedby analysing the relation between their end quadrilater-als. In carrying out the connection among road seg-ments, the basic procedure is to select one road segmentand search in backward and forward directions forother road segments that can be connected sequentially.In order to have a lower probability of selecting a falsepositive for the first road segment, a longer road seg-ment should be selected. After selecting the first roadsegment, the linking process starts. It looks in bothdirections for road segments that satisfy the followingcriteria: (1) having a road segment as reference, anotherroad segment is selected in such a way that the endquadrilaterals are closest; (2) in addition, both quadri-laterals must be close enough to each other, implyingthe necessity of selecting a threshold (e.g., 2w, where wis the mean road width); and (3) both quadrilateralsmust be collinear, implying the use of another thresh-old. Whenever a linking between two road segments isdetected, the gap between them is bridged by fittingthere a quadrilateral with the appropriate dimension.

Candidate polygons

Base polygon

Fig. 5.

Examples of possible combinations between thebase and candidate polygons.

(a)

Roadcenterline

Linking between

(b)

road seeds

?

Fig. 6.

Extraction of road seeds. (a) Extraction of roadobjects; and (b) Connection of the road.

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This means that the road is hypothesized as straightalong the region that corresponds to the detected gap.

The strategy briefly described above has two basicdifficulties. The first one concerns the possibility of thepresence of false positives (isolated road objects oreven road segments), together with the correct roadobject, which could be accepted by the three criteriapresented above. In this case, an extra criterion is nec-essary to decide which road object or segment is con-sidered correct. When this occurs, the road object orsegment is selected that better matches the road charac-teristics. The second difficulty concerns road crossings,as two road segments in high-resolution images cannotbe simply linked along road crossing regions. Thus,checking for evidence of road crossings is necessarywhile the linking process is carried out. A methodologyfor road crossing detection and reconstruction is brieflydescribed in the following section.

Figure 7 shows an example of the application of alinking process to the result generated by the road seg-ment extraction methodology (Fig. 7a). It shows onlythe polygons, representing the road edges and road cen-terline, overlaid on the image. Figure 7a shows that twoposts cause the failures of the road extraction method-ology in extracting the road that extends almost verti-cally, resulting in three disconnected road segments.Figure 7b shows that these disconnected road segmentsare combined to form a longer one.

2.2.2. Extraction of road crossings.

The extractionof road crossings is based on the results obtained by thelinking methodology, i.e., the connected road segmentsand the respective road centerlines.

Firstly, the road centerlines are used to computeapproximate positions for the road crossings, herereferred to as reference points. The computation of areference point is carried out by using two criteria, onegeometric and another radiometric. The geometric cri-

terion is based on the computation of intersectionpoints between every pair of non-collinear road center-lines that is concurrent on a road crossing, followed bythe computation of a middle point among the resultingpoints. An example is presented in Fig. 8a, where thereference point is computed by firstly linking oppositeand collinear road centerlines and then computing theintersection point (P) between the connected road cen-terlines. The radiometric criterion is in essence a con-sistency check between the mean grey level of the ref-erence point neighbourhood and the expected grey levelof the road surface.

The reference points allow the identification of theend quadrilaterals that are closest to the respective roadcrossings. These quadrilaterals are important as roadcrossings can be generically defined as the areaenclosed by the closest road quadrilaterals and the roadcrossing edges. Thus, the aim of our methodology forroad crossing extraction is to extract a polygon delin-eating the road crossing area. As shown in Fig. 8a, partof the problem can be solved by organizing trianglesdefined by the common vertex P and opposite sides thatare coincident to quadrilateral sides facing the refer-ence point P. In Fig. 8a, the road crossing is representedby a polygon combining the straight line segments

, , , and , and road crossing edgelines connecting endpoints

P

8

and

P

1

,

P

2

and

P

3

,

P

4

and

P

5

, and

P

6

and

P

7

. Figure 8b shows the final resultobtained after the application of road crossing method-ology to the results presented in Fig. 7b.

3. EXPERIMENTAL RESULTS

In order to evaluate the potential of the methodologyfor road network extraction, two experiments with realimage data are carried out. As this methodology is suit-able for road extraction from medium- and high-resolu-

P1 p2 P3 p4 P5 p6 P7 p8

(‡) (b)

Fig. 7.

Example of linking results. (a) Before the linking process; (b) After the linking process.


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tion images of rural scenes, one image of medium-res-olution and another of high-resolution are used. Baum-gartner et al. (1999) classify the image resolution in thecontext of road extraction strategies into three catego-ries: (1) low-resolution images, i.e., pixel footprintgreater than 2 m; (2) medium-resolution images, i.e.,pixel footprint ranging from 0.7 m to 2 m; and (3) high-resolution images, i.e., pixel footprint less than 0.7 m.

The first experiment (Fig. 9) is carried out with ahigh-resolution image (450

×

914 pixels) in which theroads are manifested as wide ribbons 30 pixels inwidth. This image shows two roads intercepting in a T-like form, along which some anomalies, as, e.g., per-spective obstructions caused by towers and geometricand radiometric edge irregularities, mainly along theroad part that extends between the road crossing and theinferior border of the image, are presented. This road

part is difficult to completely extract by the methodol-ogy as it is only partially compatible with the roadobjects. As shown in Fig. 9, the polygons used in theconstruction of the road segments and road crossing areoverlaid as black lines on the image.

The results obtained show that the methodologydoes not work well along the road segment that isstrongly affected by geometric irregularities. Only ashort road segment was extracted in that region. As aresult, the complete polygon representing the roadcrossing was not extracted. Along other parts of theroad network the methodology worked very satisfacto-rily, as no gaps remain. In order to carry out the numer-ical evaluation, the road centerlines were interpolatedand numerically compared to manually extracted ones.The mean distance between the automatic and manualextraction was about 3.0 pixels (or 10% of the road

(‡) (b)

P

1

P

8

P

2

P

3

P

4

P

5

P

6

P

7

P

Fig. 8.

Example of road crossing extraction. (a) Elements for road crossing detection and extraction; (b) Extracted road crossing.

Fig. 9.

Results obtained with the high-resolution image.

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width). This relatively high value is justified by the dif-ficulty of the operator in accurately extracting the roadcenterline in a high-resolution image. The complete-ness (ratio between the length of road centerlines auto-matically extracted and the length of road centerlinesmanually extracted) was about 91%. The correctness(ratio between the length of automatically extractedroad centerlines and the length of the correct ones) was100%, meaning that the part of the road networkextracted by the methodology is correct.

The second experiment was carried out with amedium-resolution image (398

×

598 pixels) in whichthe roads are manifested as narrow ribbons 7 pixels inwidth. Figure 10 shows the results obtained with thisimage. The content of the image is very favourable forthe application of the automatic road extraction meth-odology. The anomalies associated with the road net-work are very few and insignificant (e.g., small treesalong the right margin of the road extending vertically),i.e., they present no special difficulties for the method-ology. In addition to this, the road edges are well-defined and geometrically smooth. As in the previousexperiment, the roads intercept in a T-like form. A com-pleteness of about 90% was already obtained by themethodology for the extraction of road seeds. After theapplication of both completion strategies, a complete-ness of 100% was obtained, meaning that the wholeroad network was reconstructed. Besides this, the cor-rectness parameter reaches the optimal value, as it was100%. The mean distance between the automatic andmanual extraction was about 0.8 pixels, i.e., a sub-pixelaccuracy has been reached.

4. CONCLUSIONS

This paper presented an automatic methodology forroad network extraction from medium- and high-reso-lution images. It is based on two sequential phases,namely, road seed extraction and road network comple-tion. In the road seed extraction phase, the whole areaof interest is tested for local road property, and roadseeds are found. In the second phase, road networkcompletion, linking among road seeds and the recon-struction of road crossings is accomplished.

With the purpose of evaluating the method’s poten-tial in extracting road networks, two experiments werecarried out using test images: one of high resolution andanother of medium resolution. The high-resolution testimage shows unfavorable content, mainly related toobstruction and geometric and radiometric edge irregu-larities. As a result, 9% of the road network was notextracted (i.e., completeness of 91%). On the otherhand, the correctness parameter was 100%, meaningthat the part of the road network extracted by the meth-odology is correct. The content of the medium-resolu-tion test image was favorable for the application of theautomatic road extraction methodology. In fact, thecompleteness and correctness parameters were both100%. This means that the whole network was com-pletely extracted.

ACKNOWLEDGMENTS

This research was supported by FAPESP (Fundaçãode Amparo à Pesquisa do Estado de São Paulo), grantnumber 2001/01168-5, and CNPq (Conselho

Nacioal

Fig. 10.

Results obtained with the medium-resolution image.


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de Desenvolvimeto Científico e Tecnológico), grantnumber 301114/2003-0. The test images used in exper-iments were furnished by Esteio–Engenharia e Aerole-vantamentos S.A.

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Aluir Porfirio Dal Poz. Year ofbirth: 1960. Year of graduation/Nameof the institution: 1987 (CartographicEngineering)/Sao Paulo State Univer-sity. Year in which an academic degreewas awarded: M.Sc. degree in Geo-detic Science at Parana Federal Uni-versity: 1991. Ph.D. degree in Engi-neering at Sao Paulo University:1996. Affiliation: Sao Paulo StateUniversity. Position: Associate Pro-fessor. Area of research: Digital Pho-

togrammetry and Image Analysis. Number of publications: 5Book Chapters, 25 in Journals, and 75 in Proceedings. Mem-bership to academies: Scientific societies: Brazilian Societyof Cartography, Brazilian Society of Applied and Computa-tional Mathematics, and Canadian Institute of Geomatics.Editorial boards and journals: Associate Editor of the Seriesin Geodetic Science and member of the editorial board ofBrazilian Journal of Cartography. Awards and prizes forachievements in research or applications: Scientific Beginnerin Cartography (1995) and Cartographic Merit (1999), bothawarded by Brazilian Society of Cartography.

Rodrigo Bruno Zanin. Year ofbirth: 1976. Year of graduation/Nameof the institution: 2000 (Mathemat-ics)/Sao Paulo State University. Yearin which an academic degree wasawarded: M.Sc. degree in Carto-graphic Sciences at Sao Paulo StateUniversity: 2004. Affiliation: SaoPaulo State University. Position:Ph.D. Candidate. Area of research:Digital Photogrammetry and ImageAnalysis. Number of publications: 2

in Journals and 5 in Proceedings.

Giovane Maia do Vale. Year ofbirth: 1969. Year of graduation/Nameof the institution: 1998 (Mathemat-ics)/Sao Paulo State University. Yearin which an academic degree wasawarded: M.Sc. degree in Carto-graphic Sciences at Sao Paulo StateUniversity: 2003. Affiliation: SaoPaulo State University. Position:Ph.D. Candidate. Area of research:Digital Photogrammetry and ImageAnalysis. Number of publications: 3

in Journals and 10 in Proceedings. Membership to acade-mies: Scientific societies: Brazilian Society of Applied andComputational Mathematics.

automated extraction of road network from medium-and high-resolution images

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