RESEARCH ARTICLE

Automatic Road Extraction from High ResolutionSatellite Image using Adaptive Global Thresholdingand Morphological Operations

Pankaj Pratap Singh & R. D. Garg

Received: 15 November 2011 /Accepted: 8 October 2012# Indian Society of Remote Sensing 2012

Abstract Road network extraction from high resolu-tion satellite images is one of the most importantaspects. In the present paper, research experimentationis carried out in order to extract the roads from thehigh resolution satellite image using image segmenta-tion methods. The segmentation technique is imple-mented using adaptive global thresholding andmorphological operations. Global thresholding seg-ments the image to fix the boundaries. To computethe appropriate threshold values several problems arealso analyzed, for instance, the illumination condi-tions, the different type of pavement material, thepresence of objects such as vegetation, vehicles, build-ings etc. Image segmentation is performed using mor-phological approach implemented through dilation ofsimilar boundaries and erosion of dissimilar and irrel-evant boundaries decided on the basis of pixel charac-teristics. The roads are clearly identifiable in the finalprocessed image, which is obtained by superimposingthe segmented image over the original enhanced im-age. The experimental results proved that proposedapproach can be used in reliable way for automaticdetection of roads from high resolution satellite image.The results can be used in automated map preparation,

detection of network in trajectory planning for un-manned aerial vehicles. It also has wide applicationsin navigation, computer vision as a predictor-correctoralgorithm for estimating the road position to simulatedynamic process of road extraction. Although an ex-pert can label road pixels from a given satellite imagebut this operation is prone to errors. Therefore, anautomated system is required to detect the road net-work in a high resolution satellite image in a robustmanner.

Keywords Road extraction . Adaptive globalthresholding .Morphological operations .

Average intensity

Introduction

Satellite remote sensing images are capable to collectlarge volume of data to analyze global information,which is quite difficult to collect from traditional meth-ods. Some important aspects of using satellite image arethe capabilities to make available large volume of infor-mation, acquire information at difficult-to-reach regions,to monitor areas and events non-intrusively. Satelliteremote sensing techniques are quite efficient tomake available the information related to roads toimprove the transportation of suburban and urbanareas. The main purpose of this paper is to explorethe capabilities of high resolution satellite imagesfor road extraction.

J Indian Soc Remote SensDOI 10.1007/s12524-012-0241-4

P. P. Singh (*) :R. D. GargGeomatics Engineering section, Department of CivilEngineering, Indian Institute of Technology Roorkee,Roorkee 247667, Indiae-mail: pankajps.iitr@gmail.com

R. D. Garge-mail: garg_fce@iitr.ernet.in

In the last decade, the use of satellite image has beenexplored in the area of information extraction. Now adays the high resolution satellite data has become morereadily available. Therefore, it helps to identify theroads, for applications in navigation, computer visionand transportation. The high resolution satellite imagesare suitable for applying computer vision algorithms toextract the road network. Unfortunately, well-knowncomputer vision algorithms are not suitable alonefor automatically extracting the road network froma given image due to several reasons. Road seg-ments can be occluded by other nearby objectslike buildings and trees. They may also have dif-ferent spectral values and their widths may change.Moreover, junctions of unknown number of roadsand roundabouts may increase the difficulty of theextraction. Therefore, advanced methods are need-ed to extract the road network from high resolu-tion satellite images.

Road network extraction from high resolution sat-ellite images is one of the most important aspects forthree main reasons. First, the detection of results canbe used in automated map preparation. Secondly, thedetection of network can be used in trajectory plan-ning for unmanned aerial vehicles and thirdly, a wideapplication in navigation and computer vision as apredictor-corrector algorithm for estimating the roadposition to simulate dynamic process of road extrac-tion. Although an expert can label road pixels from ahigh resolution satellite image but this operation isprone to errors. Therefore, an automated system isrequired to detect the road network in a high resolutionsatellite image in a robust manner (Sirmacek andUnsalan 2010).

In the present study, a fully automated approach hasbeen used to detect the road network from the highresolution satellite imagery. In the proposed method-ology, initially global thresholding is applied forsegmentation of satellite imagery. Thereafter, mor-phological operations are applied to extract roadnetwork from the satellite imagery. This approachcan be applied to different satellite imageries toobtain the desired results for road extraction. Thereare several methods to detect the road networkfrom a given satellite or aerial image.

A method was developed to detect main roads fromsatellite images by Yang and Wang (2007). First, theydetected road primitives such as straight lines andhomogenous regions. Then, they linked detected

primitives. Unfortunately, their method cannot detectu rban roads and occ luded road segment s .Discontinuous road segments were linked using per-ceptual organization rules by Ma et al. (2007). Paralleledges are detected in panchromatic ETM (EnhancedThematic Mapper) images to locate road segments.Excellent surveys were provided on road detection inaerial and satellite images by Baumgartner et al.(1997) and Unsalan and Boyer (2005).

Satellite systems are quite attractive way to collectdata for road extraction. It also depends upon avail-ability of high resolution satellite imagery. For roadextraction applications, and in particular for identifi-cation of roads related issues, the spectral resolutionand the spatial resolution are two very importantaspects to be considered. This technique requires goodspatial, spectral, and temporal resolution to distin-guish specific urban and suburban attributes usingspace-borne technology. Specifically, this reportstates that for road extraction application (countstudies, congestion, and velocity studies) a spatialresolution of the order of 0.25–1 m and panchro-matic visible spectral resolution are necessary(Jensen and Cowen 1999; Coulter et al. 1999;McFarland 1999; Hung 2002).

Kim et al. (2000) carried out a study for the inter-pretation and detection capabilities for satellite imagesfor the spatial resolution requirements to distinguishnatural and man-made objects such as trees, shrubs,rocks, street, bridges, etc. They reported that for thedetection of paved roads, at least a 2 m spatial resolu-tion image is necessary. A spatial resolution on theorder of 0.25–0.5 m would be needed to be able todetect road networks (Coulter et al. 1999).

Methodology

In this study, a novel method is proposed to detect theroad network in high resolution satellite image. Thisproposed method is based on three main steps.Initially, adaptive global thresholding is applied tosegment roads from the cropped image (Goodman1997). Thereafter, a road network is extracted on re-moving small non-road segments by using morpho-logical operations (Li et al. 2005). A spatial roadmatrix is generated with the help of extracted roadnetworks and its similar pixel intensity values area.This road matrix indicates the possible locations of the

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non-road segment and finally, a road tracking algo-rithm is applied on the road matrix to extract only theroad networks followed by some morphological oper-ations again. Figure 1 shows a complete frameworkfor extraction of road networks.

Extraction of Road Networks

First of all, segmentation techniques are used toextract the roads from the image. To do this, twoapproaches global thresholding and morphologicaloperations are applied on satellite image. In theliterature, researchers have used Canny and Sobelmethods for edge detection, but the results werenot upto the merit. The better results are obtainedusing thresholding techniques and subsequentlymorphological operations. Before applying the seg-mentation technique, the user is to specify a regionof interest, from the original image. The region ofinterest is a part of image, which could be sub-image of rectangular shape. This proposed algo-rithm helps to provide better results in the regionsof interest instead of the whole image.

To compute the appropriate threshold values sever-al problems have to be analyzed, e.g., the illuminationconditions, different type of pavement material, pres-ence of objects such as trees, grasslands, lake,vehicles, buildings, etc. To identify most of the possi-ble problems, a high resolution satellite image is stud-ied and analyzed. This test subimage presentedvarious conditions under which the performance ofthe proposed algorithm is evaluated. In the next sec-tion the proposed algorithm is described, and theresults are discussed with the help of satellite image.

The Algorithm: Adaptive Global Thresholding BasedRoad Detection

This algorithm is based on global thresholding,which is used to segment the road networks fromthe satellite image. Figure 2a shows a typical roadnetwork. To obtain the desired results, the histo-gram of the image containing roads is analyzedand divided in four main regions, as shown inFig. 2b. Region I starts from the lowest intensityvalues to midway of Average Intensity (AvIn).

Input Image

Histogram levels

Local Thresholding

Adaptive Global Thresholding

Average Intensity distributed in four regions

Intensity based Image Segmentation

Road network Extracted Image

Morphological Operations

Binary Image Conversion

Eliminate Area, based on the no. of pixel

If area is not connected to road

Spatial road matrix and road tracking algorithm Yes

No

Morphological Operations

Fig. 1 Framework forExtraction of road networks

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AvIn is the arithmetic mean computed using allthe pixel values in the subimage.

For most of the cases, the intensity values on thisregion covers dark objects of the image such as darkvehicles, shadows, lake, muddy ponds, etc. Region IIcovers intensities that go from approximately midway ofthe AvIn to AvIn, and typically covers dark gray shadeobjects like trees, grasslands etc. Region III goes fromAvIn, to midway of highest intensities and covers brightgray objects such as asphalt concrete road, lane markers,etc. Region IV goes approximately from midway ofhighest intensity to highest intensity and generally cov-ers bright objects like concrete cement road, brightvehicles, road dividers etc. This approach assists inhandling situations in which single value thresholdingwill not work, since the threshold for each pixel dependson its location within a satellite image. Therefore, this

technique is called adaptive and said approach is calledas adaptive global thresholding.

Computing the Threshold (T) for Segmentationof Roads and Objects

After cropping the region of interest from satellite imagecontaining roads, analysis is carried out for the range ofintensities for most of the roads including values that areless than the AvIn of the image being observed. Now,the AvIn value of selected intensity range for roads fromeach row of cropped image (CI)0g (x, y) is calculated tocreate a row vector (RV). CI is a matrix, whose eachelement corresponds to the intensity value of the imageunder the study area. RV has the AvIn value of thecorresponding row’s intensity of CI. Then, the thresholdvalue T is chosen as the minimum value of RV.

The following steps of proposed threshold evaluation algorithm for segmentation is given to calculate RV (RowVector) by using CI (Cropped Image):

RV: for each row i in CI RV[ i ] = average_intensity [CI, i ] Tmin is calculated using RV:

Tmin = minimum [RV] Tmin is used to convert the test image CI to binary image BI. A pixel at any position (x y), if (x y) < Tmin,

then this pixel is considered as highway (black = 0) else

it is turned white = 1. The binary image, BI, obtained by Tmin is defined as:

[r c p] = size [ BI ]; RV = mean [ BI ]; T = minimum [ RV ]; for i = 1: r,

for j = 1: c, if I [ i,j ] < T

BI[ i,j ] = 0; end

else BI[ i,j ] = 255; end

end end

By using this proposed method, the calculatedthreshold value Tmin092.2941 is used for the test

image as shown in Fig. 3b, c shows the resulted binaryimage BI.

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Road Segment Extraction Using MorphologicalOperations (MO)

As can be seen in Fig. 3c, most road segment bound-aries are extracted. Unfortunately, there are somemissing road boundary pixels. Besides, there are alsoboundary pixels from nearby buildings and otherobjects. Up to this point, adaptive global thresholdingmethod is applied to detect the road network. Next,morphological operations (MO) are used to removesmall objects like vehicles and trees. After applying

this MO, final image is obtained. Later the intensityimage is fused with the final image to detect the roadnetwork with a value ‘0’ in binary image.

Morphological operations are based on mathematicalmorphology, which stands for removing separate part ofimage with the help of algebraic non-linear operators.This complete processing is applied on segmented im-age (resulted image after applying adaptive globalthresholding). Since any pixel less than T is made as 0to detect the roads, there are possibilities of detectingdark vehicles, shadows, and trees, which have intensity

a

b

Fig. 2 a High resolutionsatellite image of suburbanarea b Histogram of theimage in Fig. 2a

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Fig. 3 a Original Image ofroads b Image obtained afterapplying local thresholdingthe image in Fig. 3a.c Binary image (BI)obtained after applyingadaptive global thresholdingthe image in Fig. 3b.d Binary image (BI)obtained after usingMorphological operations inFig. 3c. e Intensity imageshowing roads as pixel value‘0’. f Binary image (BI)obtained from Fig. 3e.g Image showing roads,after applying a road trackingalgorithm on the road matrixin Fig. 3f. h Final Imageshowing only roads, afterapplying MorphologicalOperations in Fig. 3g

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values less than Talso. In Fig. 3c, it can be observed thatroads are dark in color. It can also be observed that thereare some isolated black pixels that correspond to darkvehicles, shadows and trees. To minimize the detectionof shadows, dark vehicles and trees, four morphologicaloperations as Clean, Majority, Fill and Holes are appliedto the binary image BI.

As a result of these four morphological operations,most of the isolated dark pixels would be eliminated.In a particular case, where large areas are covered bytrees and vegetation (with intensity values in the rangeof roads), the possibilities of their being eliminated areminimal. This is due to the fact that these large areasare not isolated set of pixels and the morphologicaloperations, which are restricted to a 3-by-3 block ofpixels, would not eliminate them. If the size of theblock is increased, there are possibilities of missingthe roads. Figure 3d shows the binary image BIobtained after applying the morphological operations.

From binary image BI (Fig. 3d) it is observed thatmost of the isolated pixels are eliminated. This pro-posed method extracts the road segments with the helpof intensity values of image, which are quite similarwith pixel values of road segment in cropped image.This process is implemented in two intricate steps toretrieve the road networks. Initially, a segmented im-age is found with the help of AvIn values of image inan iterative manner. Thereafter morphological opera-tions are used to remove non-road segments and detectmissing road segments after segmentation. This pro-cess succeeds with an accurate range of intensity val-ues to extract possible road network segment. Hence,initially a finite range of road segments is requiredfrom an exact range of pixel intensity values. Thisprocess is improved by applying adaptive globalthresholding approach, which splits the histogram ofimage in four regions. These regions are procured withthe help of pixel intensity values.

Obtaining Intensity Image of Roads

To obtain the final intensity image (Fin_Img) showingonly roads, pixels with value 0 in BI are considered. Ifa pixel has value 0 in BI, then the intensity of thecorresponding pixel in the original test image CI iscopied to Fin_Img. In this way, only the detected roadswill be copied from Img to Fin_Img using BI.Figure 3e shows the final image obtained by the algo-rithm for detection of roads.

The Fin_Img is obtained from BI (Binary Image)and T (calculated Threshold) as given below in pro-posed algorithm:

[row column page] = size [BI]; for i = 1:row,

for j = 1:column, if BI [i,j ] == 0

Fin_Img [i,j] = Img [i,j]; else

Fin_Img [i,j] = 0; end

end end

Road Tracking Algorithm on the Road Matrixto Extract only Road Networks

The proposed method is quite useful to remove non-road area, which is having similar pixel intensity val-ues as road network. It shows the novelty in thismethod. This novel method is applied on the con-verted binary image (Fig. 3f), which is the resultedintensity image (Fig. 3e). It uses the number of pixelslying in continuous and non-continuous segments withlimited width as the road network segment. This pro-posed method is explained in detail with resulted roadextracted image (Fig. 3h) in the next section.

Results and Discussion

Segmentation techniques have been used to extract theroad network from the satellite image. To obtain thedesired results in the image, the histogram is analyzedfor extraction of roads. To compute the appropriatethreshold values several problems are analyzed, forinstance, the illumination conditions, different typeof pavement material, presence of objects such asvegetation, vehicles, buildings, etc. From the analysis,the histogram is divided in four main regions usingadaptive global thresholding. This adaptive globalthresholding is used to extract the road segments fol-lowed by segmentation. MATLAB programming isused for the implementation of this proposed method-ology. Some isolated black pixels are obtained thatcorrespond to dark vehicles, shadows, and trees. Theproposed method is used to minimize the detection ofaforesaid pixels by applying some morphologicaloperations.

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A spatial road matrix is generated with the helpof extracted road networks and its similar pixelintensity values area. This road matrix indicatesthe possible locations of the non-road segmentsby calculating unequal width of area which hassimilar intensity pixels values as the road segment.The width of area depends on the occurrence ofno. of pixels in row and column vector. If it is notconnected with a regular pattern of similar widthof area, consequently it can be easily identified asnon-road segment. If connected, it is identified asroad segment with unequal width of area. Thespatial road matrix and road tracking algorithmare processed in an iterative manner to detectnon-road area. Thereafter, a pixel value is assignedas ‘255’ for detection of non-road area, which isshown in Fig. 3g.

Finally, the proposed hybrid method is applied withmorphological operations on resulted image (Fig. 3g),to extract only the road networks as shown in Fig. 3h.Figure 3h shows the accurate road network with thehelp of proposed hybrid method. The manually delin-eated road region drawn by an expert is taken as theground truth in Fig. 4 and compared with the results ofproposed road extraction hybrid method in Fig. 5. Theperformance of the proposed road extraction techniqueis evaluated using six quantitative parameters likesensitivity, specificity, completeness, correctness,Jaccard index, Dice coefficient. These parameters areused to evaluate the performance of the road detectionfrom the high resolution satellite image. Sensitivitymeans percentage of road pixels properly included insegmentation results out of all the pixels. Specificitymeans percentage of road pixels properly excludedfrom the segmentation results out of all the pixelsoutside of ground truth road.

These parameters are defined as follows:

% Sensitivity ¼ TP

TPþ FN

� �� 100 ð1Þ

% Specificity ¼ TP

TNþ FP

� �� 100 ð2Þ

TP or true positive means a pixel appearing in bothmanually segmented road area and road area detectedby proposed hybrid method. TN or true negativemeans a pixel absent in both manually segmented roadarea and road area detected by proposed hybrid meth-od. FP or false positive means a pixel absent in man-ually segmented road area but appearing in road areadetected by region-growing method. FN or false neg-ative means a pixel appearing in manually segmentedroad area but absent from road area detected by pro-posed hybrid method (Metz 1978).

Fig. 5 segmented proposed hybrid method road extraction

Table 1 Quality assessment parameters for road network extraction

Completeness Correctness Sensitivity Specificity Jaccardindex

Dicecoefficient

95.32 96.52 95.32 96.56 92.15 95.92

Table 2 Quality measurement parameters for road networkextraction

Accuracy VSGT VAGTMO

95.94 507.71 501.40

Fig. 4 segmented ground truth road network

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Mathematical analysis includes the completeness andcorrectness to evaluate the accuracy of the extractedroad network (Heipke et al. 1997). Completeness isdefined as the percentage of the reference data whichis detected during road extraction

Completeness ¼ length of matched reference

length of reference¼ TP

TPþ FNð3Þ

Correctness represents the percentage of the extractedroad data which is correct

Correctness ¼ length of matched extraction

length of extraction¼ TP

TPþ FPð4Þ

Jaccard index is a statistical measure of similaritybetween sample sets. For the two sets, it is defined asthe cardinality of their intersection divided by thecardinality of the union. Like the Jaccard similarityindex, the Dice coefficient also measures the set agree-ment. It gives twice the weight to agreements. For thetwo sets, it is defined as the cardinality of their inter-section divided by the cardinality of the addition. Avalue of 0 indicates no overlap and 1 indicates theperfect agreement. Higher numbers indicate betteragreement.

Jaccard Index ¼ SISGT \ SIAGTMO

SISGT [ SIAGTMOð5Þ

Dice coefficient ¼ 2� SISGT \ SIAGTMO

SISGT þ SIAGTMOð6Þ

Where, SISGT represents manually segmentedground truth road area, SIAGTMO represents road areadetected by proposed hybrid method. Table 1 shows thesimilarity coefficients results significantly good for thisproposed hybrid approach for road extraction.

Another three parameters like Accuracy, VSGT andVAGTMO are evaluated for quality measurement.Accuracy means proportion of correctly segmentedroad pixels out of all pixels of segmented ground truthroads. VSGT gives number of pixels in segmentedground truth voxel (region pixels) and VAGTMO givesnumber of pixels in segmented proposed hybrid voxel.

Table 2 shows that better accuracy is achieved byusing this approach. In Table 2, VSGT and VAGTMO

parameters are representing occurrence of pixels insegmented ground truth and segmented proposed hy-brid approach respectively.

Conclusions

The proposed method of integration of adaptive globalthresholding and morphological operations is used toextract the road network from a high resolution satel-lite imagery. The major and practical merit of thisapproach is introducing adaptive global thresholdingfollowed by morphological operations. Extraction ofroad segments has been done with the help of adaptiveglobal thresholding, which is based on threshold limitand pixel intensity values. Then, by applying morpho-logical operations on the segmented image, the pro-posed method is tested on high resolution satelliteimagery of suburban area. Experimental results indi-cate possible use of algorithm in extracting the roadnetwork from high resolution satellite images in areliable manner. In some cases, small part of barrenland and parking area is classified as roads. The ap-proach is based on the pixel intensity values, whichinduces the detection of some unwanted objects. Thesenon-road segment areas are also removed using roadtracking algorithm. The quality assessment parameterscorrectness and completeness values are calculated as96.52 and 95.32 respectively, which show good degreeof extraction of road network. An accuracy value of95.94 also shows better extraction. The occurrence ofpixels in segmented ground truth and segmented hybridapproach also has good matching. Still there is a re-quirement of some soft computing techniques to resolvethe problem of extraction of non-road segments to im-prove this automated technique.

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