5 ijaest volume no 3 issue no 2 automatic extraction of road networks 115 121

8/7/2019 5 IJAEST Volume No 3 Issue No 2 Automatic Extraction of Road Networks 115 121

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Automatic Extraction of Road Networks based on

Normalized cuts and Mean Shift method for high

resolution Satellite Imagery

M. Rajeswari*

Department of Telecommunication Engineering Bangalore Institute of Technology

Bangalore, 560 004, KA, India

E-mail: [email protected]

K.S.GurumurthyDepartment of Electronics & Communication Engineering

University College of Engineering,

Bangalore, 560 001 KA, India


S.N. Omkar, J. SenthilnathDepartment of Aerospace Engineering

Indian Institute of Science Bangalore, 560 012, KA, India

E-mail: [email protected], [email protected]

L. Pratap Reddy

Department of Electronics & Communication Engineering

JNTU College of Engineering, Kukatpally

Hyderabad-500 085 AP, India


Abstract — To evaluate the speed of growth of an urban area road

is one of the fast information updating element during urbandevelopment. Road information extraction based on high

resolution satellite images play an important role because roads

affect city land usage. In this paper, two approaches for road

network extraction for an urban are proposed. Most research in

road extraction begins with an original image. It is difficult and

computationally expensive to extract roads due to presences of

other road-like features with straight edges. In the proposed

method firstly the image is preprocessed to improve the tolerance

by reducing the noise (the buildings, parking lots, vegetation

regions and other open spaces). Secondly roads are extracted

based on Normalized cuts method and Mean Shift Method.

Finally the accuracy for the road extracted images is evaluated

based on quality measures. The experimental results show that

these approaches are efficient in extracting road segments inurban region from high resolution satellite images.

Keywords- Road extraction; Filtering; Mean shift; Normalized

cut; performance evaluation

I. I NTRODUCTION

Analysis of high resolution satellite images has been animportant research topic for accurate and up-to-date roadnetwork information essential for urban planning. Automatedmethods improve the speed and utility for road mapping andare therefore highly desirable. Fully automatic extraction of roads from satellite imagery does not require human

intervention to perform time consuming and expensive processof mapping roads and has been an active research anddevelopment topic for the last twenty years. Many approachesfor the automatic extraction of roads from satellite imagery arefound in the literature. Zhang, [1] have done databaseverification and updating determining the region of interest for roads by a multispectral classification and excluding highregions using Digital Surface Models (DSM) then paralleledges are extracted in the regions of interest. For rural areasMena et al., [2] have used three different classification methodsfor color and texture and are combined to extract road regions.Roads are only extracted in the regions around database roads.

Road extraction is difficult in the presence of context

objects such as buildings or trees close to the road, disruptingthe appearance of the road or occluding it. Baumgartner et al.,[3] have considered context objects. For urban areas Zhang etal. [4] have developed their extraction based on multispectralclassification and filtering using shape criteria. DSM fromLIDAR is used as an additional data source by Hu, et al., [5] torestrict the search space for the straight lines. This cannothandle curved roads well. In region based extraction Doucette,et al., [6] used hyper spectral data channels to extract roadregions and road pixels are grouped into a network with a k-median classification. Hu, et al., [7] extracted road footprints based on their shape and then track them. Junction footprintsare distinguished from ordinary road footprints. They have

used Post-processing to remove false extractions. Bacher, et al.[8] calculated value for the road class pixel then roadhypotheses is determined using fuzzy logic. Road network isgenerated using weighted graph and detour factor to closesmall and large gaps respectively. Poullis et al., [9] haveextracted road using Gabor filter for image classification intoroad and non-road pixels segmentation using graph cut andGabor filter for post processing

In literature on road extraction using mean shift Zhanfenget al., [10] extracted segments from the image based ondifferent scales, and are analyzed using transcendentalknowledge. In Sun [11], the user inputs a set of nodes of theroad and then the partitioning of the image is done using Mean

Shift Method and roads are extracted between the nodes usingFast Marching Method. Yao-Yi [12] and Simler [13] appliedmean shift as a filter to reduce the number of colors in the inputimage. Guo [14] applied mean shift to determine averagesaturation to classify the object. Saturation image is noisy andthe road boundary is not recognizable. Hence post processing isused to extract roads.

In literature on road extraction using Normalized cuts,Qihui et al., [15] have used normalized cuts for initialsegmentation step also requires priori information like depthand intensity measurements from the range sensor for LIDAR

M. Rajeswari et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIESVol No. 3, Issue No. 2, 115 - 121

ISSN: 2230-7818 @ 2011 http://www.ijaest.iserp.org. All rights Reserved. 15



data and then extracts roads using hypothesis testing. Groth etal. [16, 17] have applied Normalized Cuts algorithm for dividing image into Segments with color and edge criteria.Then, the initial segments are grouped to form larger segmentsand are then evaluated using shape criteria to extract road parts.

In this paper, we propose an approach for road network extraction in urban areas based on our previous work [18]without the priori information about road. Main objective of this work is to show the effectiveness of the approaches for road extraction. Initially image is pre-processed to removesmall linear structures appearing as noise then the normalizedcuts and Mean shift methods are used to extract the roads. Thequality measures evaluated show that these approaches areaccurate and robust for the extraction of roads.

The proposed road network extraction process with Normalized cut and mean shift algorithms is described insection II. Section III describes the Performance evaluation.Section IV describes the experimental results with comparisonand conclusions and future work are given in section V.

II.

METHODOLOGY The proposed road extraction methodology has been

broadly divided into two steps: the pre-processing andsegmentation for road extraction.

A. Pre-processing

Pre-processing is required to improve the image quality andto generate the elongated road network for further processing.Firstly, the panchromatic image was grouped into 15 clusters inan unsupervised classification. This was followed by agrouping operation to reduce the noise and smoothening of theimage. This improves the tolerance as the noise is reduced andhence minimizes the effects of using a high resolution imagery

(at such high-resolutions the road is susceptible to noise in theform of vehicular traffic, road markings, structural shadows,occlusions and many road surface contrast irregularities).Filtering operation generate the elongated road regions.

1) ClassificationThe images used are Quick-bird high resolution (2.4m and

0.6m) satellite images of the Bangalore urban area, India. Thedimensions of the images are 1000X943 and 627X630 pixels.By setting the threshold value of 0.6, the high resolution imagewas grouped into 15 clusters in an unsupervised classification,five of which correspond to road networks. A level oneclassification was carried out by dividing the image into twoclasses: Roads and non-roads.

2) Grouping A nearest neighborhood grouping (NNG) operation was

applied to the classified image for smoothening the spectralresponse within the pixel‟s local neighborhood. In this processthe vehicular occlusions and small patches of road contrastabnormalities were eliminated. In this method a pixel is chosento begin with and its surrounding eight neighbors areconsidered for voting. If a class gets four or more votes, thenthe chosen pixel is assigned to that class. Otherwise the pixelretains its class. Road pixels in the neighborhood grow over

such noise elements and homogeneous regions or segments aregenerated.

3) Filtering The method of road extraction in urban areas poses

challenge because the spectral reflectance of some of the old buildings (or buildings with a type of construction that rendersa dark road like effect) resembles the road surface. Such buildings form the clutter and these non-road structures need to be removed. Median filtering technique is applied. Medianfiltering is similar to using an averaging filter, in that eachoutput pixel is set to an average of the pixel values in theneighborhood of the corresponding input pixel. However, withmedian filtering, the value of an output pixel is determined bythe median of the neighborhood pixels, rather than the mean.The median is much less sensitive than the mean to extremevalues (called outliers). Median filtering is therefore better ableto remove these outliers without reducing the sharpness of theimage.

B. Segmentation for Road Extraction

Image segmentation provides a powerful tool to extract

features such as texture and shape from objects. Imagesegmentation is a process of partitioning the image into non-intersecting homogeneous regions on neighboring pixels, andno pairs of contiguous regions are homogeneous. In the proposed method for road extraction Segmentation methods are based on Normalized cuts and mean shift.

1) Normalized cuts Method Normalized cuts given by Shi and Malik [19] is a graph

based method using grouping of pixels maximizing similarityof pixels belonging to the same segment and minimizingsimilarity of pixels belonging to different segments. In optimalsegmentation, an image is divided into two optimal segmentsand then recursively segmented. Ncut (normalized cut)measures the similarity between two segments. Nassoc(normalized association) measures similarity between pixelswithin a segment then maximizing Nassoc and minimizing Ncut. Normalized cut is an NP-complete problem so it isapproximated using an Eigenvector equivalent problem.

In normalized cuts, only the boundaries are considered. Theadvantage of this is that hard constraints are not needed to gaininformation about roads. This makes normalized cuts moreconducive for automatic road extraction, but also limits the potential extraction that this method can produce because auser only has limited input in determining the extraction. Thenormalized cuts algorithm defines the optimal extraction interms of an extraction that divides the image into two parts. To

achieve a full extraction, initially an optimal binary extractionis performed and then recursively an optimal extraction withineach extraction is determined.

Given a measure of similarity between pairs of pixels,optimal extraction is defined as maximizing the similarity between pixels belonging to the same set, and minimizing thesimilarity between pixels that belong to different set.

The optimization is defined as follows:

Let G = (V, E) be a graph, where V is the set of pixels P,and E is the set of all edges {p, q} between every pair of pixels


ISSN: 2230-7818 @ 2011 http://www.ijaest.iserp.org. All rights Reserved. Page 116



p and q. Set the weight of an edge, denoted by w{p,q}, equal toa similarity measure between the pixels p and q, whereashigher weight corresponds to pixels that are more similar. Thesimilarity measure is:

(1)

where Xi is the spatial location of i and F i is the vector based on intensity information at i. This gives the similarity between two pixels to be proportional to the probability of pixel i being generated by a Gaussian distribution centered at pixel j. For this a fully connected graph is not required, onlyedges between two pixels that have a positive weight. Tomeasure the similarity between sets, normalized cut is given by

),(

),(

),(

),( =Y) Ncut(X,V Y assoc

Y X cut

V X assoc

Y X cut

V q X p

q pw,

),(=V)assoc(X,

V qY p

q pw,

),(V)assoc(Y,

Where assoc(X,V) is the total connection from nodes in Xto all the nodes in the graph and assoc(Y,V) is the totalconnection from nodes in Y to all the nodes in the graph.

The advantage of using the normalized cut is thatminimizing Ncut(X,Y) maximizes a measure of similaritywithin the sets X and Y resulting in optimal partition. Let W bethe cost matrix, i.e. W(i,j) = ci,j; is the weight between thenodes i and j in the graph and D be the diagonal matrix such

that D(i,i) = j jiW ),( is the sum of costs from node i and

D(i,j) =0. Based on this input, the optimal partition can befound by computing:

such that 01and,10,,1 D ybbi T

If y is allowed to take real values then the minimization of equation (10) can be done by solving the generalized eigenvalue system.

The minimum can be found by calculating the Eigenvectors of

a matrix derived from the W-matrix using equation (1). The

“generalized” Eigen system in (6) can be transformed into a

“standard” Eigen value problem as given below

Steps of normalized cut algorithm are summarized asfollows:

Consider a similarity measure and compute matrices D

and W.

Solve the generalized Eigen value equation from

Equation (6) for the eigenvector y associated with thesecond smallest Eigenvector.

Find a splitting point of „y‟ such that the normalized

cut is minimized and use it to partition the graph. Recursively partition each segment until some

sufficient measure of similarity is reached inside thesegment.

2) Mean shift method Mean shift consists of two main steps [20], Filtering step

and Segmentation step

Filtering step:

use a kernel density estimator (weighted average e.g.Gaussian) to shift the means of pixels in the image

stop when each mean sequence has convergedSegmentation step:

use the converged means to delineate sets basically a pair of pixels belong to set if their

convergence color and spatial components are

within some given range

for sets with fewer pixels than a given threshold, place them into neighboring sets

The mean shift technique [20] is based on unsupervisedclustering. Clustering is a technique used to classify a large

amount of data into different categories. It is unsupervised asthere is no information indicating the correct group, for example, for most image extraction problems there are no pixels marked as being part of a specific object. It iteratively

shifts the mean of a pixel resulting in the pixel being drawnto a local point of convergence

Mean shift starts with a mean for each pixel. The means

are shifted by taking a weighted average over all the pixelsin the image. The weighted average is called a kernel densityestimator. Essentially, it is a function that, given a point x in

the domain, weights all the points in the domain based ontheir distance to x. Kernel density estimation is based onGaussian. By iteratively shifting the mean based on thekernel, all the pixels get drawn to a number of local points of

convergence. Segments are defined by grouping together

otherwise0

if

,

22

i N jee

jiW

X ji F ji X X F F

wherematrix, symmetric N N W

Dy yW D

Dy y

yW D yY X Ncut

T

T ymin),(min

Z Z DW D D

2

1

2

1

y DwhereZ 2

1





pixels whose mean converges to similar intensities. Themovement of each mean to its point of convergence can becompared to a particle moving through a force field until it

reaches an equilibrium state, or can be seen as the pointsmoving toward peaks and valleys in a landscape. In this process there is clustering where the number of sets isdetermined by the image itself and there is no artificialinitialization of sets.

Given n data points xi, i=1,…,n in the d-dimensional space

Rd, the kernel density estimation at the location x can be

calculated by

2

xx

1

11xˆ

ih

ik

ihn

n

i

d K

with the bandwidth parameter hi > 0. The kernel K is a

spherically symmetric kernel with bounded support satisfying

[17],

1x0xx2

, k c K d k

where the normalization constant ck,d assures that K(x)

integrates to one. The function k(x) is called the profile of the

kernel. Assuming derivative of the kernel profile k(x) exists,

using g(x) = -k’(x) as the profile, the kernel G(x) is defined as

G(x) = c g,d g(||x||2 ). The following property can be proven by

taking the gradient of Equation (8) as follows,

)x(ˆ

)x(ˆ)x(

G

K G

f

f C m

where mG(x) is called mean shift vector. C is a positive

constant and, it shows that, at location x, the mean shift vector

computed with kernel G is proportional to the normalized

density gradient estimate obtained with kernel K . The mean

shift vector is defined as follows

x

h x x g

h

x x g x

xm

n

i

i

n

i

i

i

Gh

1

2

1

2

, )(

The mean shift vector thus points toward the direction of

maximum increase in the density. The mean shift procedure is

obtained by successive computation of the mean shift vector

and translation of the kernel G(x) by the mean shift vector.

Finally, it converges at a nearby point where the estimate has

zero gradients [20]. The iterative equation is given by

,...2,1

11

2

2

1

2

2

1

j

h

x y g

h

h

x y g

h

x

n

i

i j

d

i

n

i

i j

d

i

i

j(12)

The initial position of the kernel (starting point to calculate

y1) is chosen as one of the data point xi. Usually, the modes(local maxima) of the density are the convergence points of

the iterative procedure.

Steps of mean shift algorithm are summarized as follows:

Initialize

Compute the mean shift vector until convergence.Look for the mode, in which the mentioned pixel (p)converges. This is carried out by using a uniformGaussian kernel.

Assign in Zi(filtered image)the z component of thecalculated value (intensity of the level grey).Run themean shift filtering procedure for the image and

store all the information about the d-dimension

convergence point in Zi. Define the regions that are in the spatial domain (hs)

and that its intensities are smaller or equal than hr/2(range domain).

For each of the defined regions in the previous steplook for all the pixels belonging to it and assign in Z

the mean of its intensity values.

Build the region graph through an adjacent list of the following way: for each region, look for alladjacent regions that are on the right hand side and below.

While there exists nodes in the graph, which have been not visited, are given as parameter,

Go to assigning intensity values step until no more pixel value modifications.

III. PERFORMANCE EVALUATION

The automatically extracted roads are compared withmanually traced reference roads using ERDAS to performaccuracy assessment. Since roads have linear features, it is

possible to use all the data rather than just sample points toconduct the accuracy assessment. In [21] several qualitymeasures to evaluate the quality of extracted roads is proposed.

The measures for accuracy assessment of road extraction are:

TNTP

TP

ssCompletene

FNTP

TP

sCorrectnes





FNFPTP

TP

Quality

TP

FN]FP-[1-TPRe

dundancy

N

ref der d

RMSE

N

i

i

1

2)(

N=number of pieces of unmatched extraction

km]reference[of Length

n =Gaps/kmof Number

n=number of gaps

n

li g n

i

1

)(

=length[m]gapMean

g(li) = length of the ithgap(m)

Completeness represents the percentage of reference data being correctly extracted. Correctness indicates the

percentage of correctly extracted roads. Therefore,completeness is producer‟s accuracy and Correctness isusers‟ accuracy. The quality represents the overall

accuracy Redundancy gives the percentage of correctlyextracted roads which overlap as they correspond to thesame ideal road RMSE (Root Mean Square error)expresses the geometrical accuracy of extracted roads, the

number of gaps per unit length and the mean gap length,where a gap corresponds to a part of the ideal road that isnot found. To calculate these quality measures, buffer zones are generated around the extracted roads and thereference roads. The chosen buffer width is approximatelyhalf of the actual road width. The direction difference is

derived directly from the vector representations of bothnetworks. A true positive (TP) is where the derived resultcoincides with the reference result. A false positive (FP) is

where there is a road pixel in the derived result that is notin the reference data. A false negative (FN) is where thereis a road pixel in the reference data that is not present inthe derived result.

IV. EXPERIMENTAL RESULTS

The images used are high resolution Quick-bird (0.6mand 2.4m) satellite images of the densely populatedBangalore urban area, India. The dimensions of theimages are 1000X943 and 627X630 pixels. Thealgorithms were successfully tested and compared on the

Quick-bird image. Figure 1 shows: (a) the input image1000X943; (b) Preprocessed image (c) The extracted roadnetwork using normalized cuts with number of segmentsset to 5 (d) segmentation produced by mean shift with bandwidth =10 and minimum region=100.Figure 2 shows:(a) the input image 627X630; (b) Preprocessed image (c)The extracted road network using normalized cuts withnumber of segments set to 8 the segments (d)

segmentation produced by mean shift with bandwidth =10and minimum region=400. The experiments wereconducted on Intel core2 duo CPU T5550 with 1.83 GHzwindows XP system using Matlab 7.4.

For mean shift extraction since the preprocessed image

is used post processing steps are not required like in other works[12][14]. Mean Shift is a region-search method Itfilters the image by representing each pixel with a vector that combines both image location and pixel values and

assigns each pixel the values of the nearest maximumdensity location for the combined vector. The maximumdensity points are clustered and gradient ascent method is

used for finding the local maximum of a target kernel-

density. When the kernel function, k, is differentiable,convergence is guaranteed.

Earlier approaches have used normalized cut [15, 16,

and 17] for initial segmentation and multiple processingsteps were applied on the segmented image to extract roadsegments. In this paper the normalized cuts algorithm is

applied for the pre processed image to extract the roadsegments. As Normalized Cuts algorithm takes both localand global characteristics of the image are taken intoaccount. Local characteristics are incorporated into the

similarity matrix which considers the similarity of pixelsin a close neighborhood. Global characteristics come into play when the best cut is computed: a global minimum

criterion must be met. The combination of local andglobal aspects is a very important aspect of the Normalized Cuts algorithm. With this the algorithm

ignores noise, small surface changes and weak edges. Theresults show that the extraction is successful as mostsegments cover only roads. Normalized cut uses spectral method and Mean Shift

uses clustering method of tracking and both are iterativemethods. For straight and long roads the boundaries arerelatively easily obtained by both methods. Figure (2c) has

several road segments and also contains a non roadsegment in the lower part of the left side of the image.Since the segments are defined by the image content andother objects have similar radiometric and geometric

properties as that of road resulting in false road. Figure(1d) and (2d) show that mean shift results contain small,random elements as part of the final extraction.





Figure 1: (a) Pan sharpened image 1000 X 943 (b) Preprocessed image (c)

Normalized cut Extracted image (d) Mean Shift Extracted image

Figure 2: (a) Multispectral Image 627 X 630 (b) Preprocessed image (c)

Normalized cut Extracted image (d) Mean Shift Extracted image

TABLE 1: COMPARISON OF PERFORMANCE MEASURES ON 2 IMAGES

For the extracted road network , the accuracy of theextraction results are based on the road extraction metrics

proposed in [21], which include the completeness,correctness, quality, redundancy, root-mean-square (RMS)

and the gap statistics. Manual extraction data was obtainedusing ERDAS tool. Table 1 shows the numeric results. Theaverage completeness are from 80.69% to 96.09%, the

average correctness are from 67.13% to 73.7%, the qualityfrom 57.84% to68.87%and the average redundancy arefrom 1.432% to 1.728% for the satellite images . Figure(1d) shows an example result, where the geometry of theextracted road are close to the road in the original image.

Some lines are not connected causing lower completeness(80.69%) and correctness (67.13%). Since parts of non-roadfeatures are also extracted redundancy is more becausesome of the road lines were extracted as shorter linesegments with small orientation variation(1.728%) for high

resolution image using mean shift .For low resolutionimage figure (2d)the mean shift produces results better thannormalized cut results(96.09% completeness and 73.7%

correctness). The average RMS difference and Mean gaplength of mean shift are higher for low resolution imagesusing mean shift compared to high resolution images. Onthe whole mean shift performs better than normalized cuts

for 2.4m resolution image and for 0.6m resolution imagenormalized cut.

V. CONCLUSIONS

In this paper we have proposed two approaches toaccurately extract the road networks. Pre-processing is used

to eliminate the noise exploiting the structural informationin the image and then normalized cut method and meanshift methods are applied leading to the extraction of elongated road segments.

The main advantage of using normalized cut method isthat both intergroup and intragroup characteristics of theimage are taken into account. Intragroup characteristics are

incorporated into the similarity matrix which considers thesimilarity of pixels in a close neighborhood. Intergroupcharacteristics come into play when the best cut iscomputed Mean Shift is a region-search method which

filters the image by representing each pixel with a vector combines both image location and pixel values to assigneach pixel the values of the nearest maximum densitylocation for the combined vector. Then the maximumdensity points are clustered.

The results obtained show robust modeling capability and

better quality measures compared to earlier methods and aresuited for automatic road extraction. Looking at the resultsshown in Table 1 we conclude that normalized cutsalgorithm produces the better extraction for high resolution

images and mean shift for low resolution images.There are few limitations in both the methods.

Normalized cut divides the image into segments of equal

size. Hence the recursion is not stopped appropriately andthe numbers of segments have to be specified for eachimage. We are trying to estimate the appropriate number of

segments from the given image. Normalized cut codehandles images that have about 1, 00,000 pixels. For this,the input images were down sampled before the

Quality Measure Image1(1000X943)

0.6m Resolution

Image 2(627X630)

2.4m Resolution

Normalized

Cut

Mean

Shift

Normalized

Cut

Mean

Shift

Completeness% 96.09 80.69 82.03 95.18

Correctness% 70.86 67.13 73.7 73.23

Quality% 68.87 57.84 63.46 72.19Redundancy 1.451 1.728 1.575 1.432

RMS (m) 3.978 18.8 56.84 32.58

Number of gaps per (km) 0.763 5.34 0.1934 0.1934

Mean gap length (m) 0.04467 0.0631 0.472 0.14





extraction. Also the images were converted to grayscale before the extraction. The Mean Shift method hasradically symmetric kernels. Hence the change in the road

width requires an adjustment of the kernel bandwidth toconsistently track the road. Our future work includesovercoming these limitations

ACKNOWLEDGMENT

This work was supported by the Space Technology Cell,Indian Institute of Science and Indian Space ResearchOrganization grant.

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