28.IJAEST Vol No 5 Issue No 2 Raod Extraction Using k Means Clustering and Morphological Operations 290 295

8/7/2019 28.IJAEST Vol No 5 Issue No 2 Raod Extraction Using k Means Clustering and Morphological Operations 290 295

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Rohit Maurya, Dr. Shalini Singh, Dr. P.R Gupta, Manish Kumar Sharma

Centre for Development of Advance Computing

B-30, Institutional Area, Sector- 62, Noida, India

Email: [email protected]




Abstract — In this paper we proposed the method for road

extraction. The road extraction involves the two main steps: the

detection of road that might have the other non road parts like

buildings and parking lots followed by morphological operationsto remove the non road parts based on their features. We used

the K-Means clustering to detect the road area and may be some

non road area. Morphological operations are used to remove the

non road area based on the assumptions that road regions are

an elongated area that has largest connected component.

Index Terms — K-Means clustering, morphological

operations, road extraction, segmentation.

I. INTRODUCTION

HE road extraction from digital images has drawn a

special attention in the last few decades. Numerousmethods has been developed which includes semi automatic

and automatic road extraction. Road extraction plays a very

important role in vehicle navigation system, urban planning,

disaster management system and traffic management system.

Semi automatic road extraction required requires user

interaction in order to extract the road where automatic

method requires no user interaction.

In existing method of road extraction various semi

automatic and automatic methods have been developed. Karin

K. Hedman, U. Stilla, G. Lisini, P. Gamba (2010) [1] has

used two road extractors one for rural areas and another for

urban areas. They used two steps for road extraction: first isline extraction followed by a smoothening and splitting step

and in another step linear features are evaluated on their

attributes using Bayesian probability theory. Hui Kong, J.-Y.

Audibert, J. Ponce (2010) [2] has developed the method based

upon the vanishing point associated with main part of road,

followed by the segmentation of the corresponding road area

based upon the detected vanishing point. Anil and Natarajan

(2010) [3] have developed the method based upon statistical

region merging [9] for image segmentation and road network

is

Figure 1. Roads in developing suburban area

extracted based upon skeleton pruning method based on

discrete curve evaluation. Yinghua He, Hong Wang, Bo

Zhang (2003) [4] has developed an algorithm composed of

the segmentation of the corresponding road area based upon

the detected vanishing point. Anil and Natarajan (2010) [3]have developed the method which uses statistical region

merging [9] for image segmentation and road is extracted

using skeleton pruning which is based on contour

partitioning. Yinghua He, Hong Wang, Bo Zhang (2003) [4]

has developed an algorithm consists of two major points:

boundaries are estimated based on the intensity image and

road areas are detected based on the full color image. Tomoko

Tateyama, Zensho Nakao, Xian Yan Zeng, Yen-Wei Chen

Road Extraction Using K-Means Clustering and

Morphological Operations

T

Rohit Maurya et al. / (IJAEST) INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING SCIENCES AND TECHNOLOGIESVol No. 5, Issue No. 2, 290 - 295

ISSN: 2230-7818 @ 2011 http://www.ijaest.iserp.org. All rights Reserved. Page 290



(2004) [5] has proposed a method which combines color,

texture information and shape information for segmentation

of high resolution images and a new direction filter which

focuses on road features having information on specific

directionality. The method uses color and texture information

for global segmentation and shape information for local

analysis. Onur Tuncer (2007) [6] has proposed a method

which consists of preprocessing the image via the series of

wavelet based filter banks and reducing the yielding data into

a single image, then utilizing a fuzzy inference algorithm to

find road areas. H. Mayer, I. Laptev, A. Baumgartner and C.

Steger (1997) [7] has used ribbon snakes not only to extract

the roads eficiently, but also used to bridge gaps in the

extracted roads due to shadows created by building and trees.

Q. Zhang and I. Couloigner (2006) [8] has proposed a

method which starts with an image segmentation using a

spectral clustering algorithm. This step focuses on the

exploitation of the spectral information for feature extraction.

The road clusters are automatically identified using a fuzzy

classifier based on a set of predefined membership function

for road surface and the corresponding normalized digital

numbers in each multi spectral band. A number of shape

descriptors from the refined angular texture signature are

defined and used to reduce the misclassification between

roads and other spectrally similar objects such as parking lots,

building or crop field. Jalal (2006) [10] has proposed a

method comprise of two stages: feature extraction and fuzzy

modeling for road identification. In the first stage mean,

standard deviation, skewness and kutosis. In the fuzzy

modeling stage, the roads are identified based on converted

features to the specific fuzzy sets in the linguistic variables.

In this paper we use the K-Means clustering based

segmentation to find the road cluster followed by

morphological operations to filter the area which has similar

features as the road like buildings, parking lots and crop field.

II. METHODOLOGY

The flowchart of road extraction is given in figure 2. This

method includes four parts: segmentation using K-Means

clustering, road cluster identification, road cluster filtering

and result evaluation.

Figure 2. Flowchart of road extraction method

A. Image segmentation using K-Means clustering:

Clustering is method to group similar objects into one

cluster. K-means clustering [11] finds clusters such that

objects within each cluster are as close to each other as

possible, and as far from objects in other clusters as possible.

K-means clustering requires you to specify the number of

clusters to be partitioned and a distance metric to specify how

close two objects are to each other.

We used K-Means to cluster the objects into clusters using

the Euclidean distance metric and to segment the image in

various clusters.

K-means Clustering:

As described by Suman Tatiraju and Avi Mehta [14]

K-Means algorithm is an unsupervised clustering algorithm

that classifies the input set of data into multiple clusters based

on their distance from each other. Based on the distance

metric specified the algorithm tries to group the input data

into various clusters . The points are clustered around

centroids µi ∀ i = 1... k which are obtained by minimizing

the objective

k

V = Σ Σ (x j - µi)2

i=1 x j ϵ Si

where there are k clusters Si, i = 1, 2, . . . , k.

µi is the mean point of all the points x j ϵ Si.

The algorithm has following steps:

1. Compute the intensity distribution of the intensities.

2. Choose k centroids randomly.

3. Repeat the following steps until the cluster does not

change anymore.

4. Cluster the points based on distance of their intensities

from the centroid intensities.

c(i) := arg min || x(i) - µ j ||2

j

5. Compute the new centroid or mean point for each

clusters.

m

Σ 1{ c(i) = j } x(i)

µi := i=1

m

Input Image

Segmentation using K- Means clustering

Road cluster identification

Road cluster filtering

Result evaluation

Extracted roads





Σ 1{ c(i) = j }i=1

Where k is the number of clusters to be found, i iterates over

the all the intensities, j iterates over all the centroids and µi

are the centroid intensities.

Figure 3. Segmented image with road cluster shown

in red

B. Road cluster identification:

After input image segmentation, we have three clusters as

an output so to make the approach fully automatic we should

know which cluster is the road cluster. Road cluster is need to

be identified because we need the right cluster in order to

extract the roads otherwise our result we cannot get the right

output.

To identify the road cluster we used the concept that road

usually appear as elongate regions. So the cluster which has

the longest connected component will be the road cluster.

Figure 4. Road cluster after segmentation

C. Road cluster filtering:

The road cluster which is obtained from the classification is

consists of roads, buildings, parking lots and other objects

which has similar feature like a road. Further processing is

needed in order to remove the non-road areas before road

extraction. In this work the road cluster filtering approach

Figure 5 Extracted Roads after refinement step

based on the concept that roads usually appear as elongated

areas while the object similar to the roads are usually open

areas.

In this step the following operations are performed.

1. Dilation- Dilation refers to expansion i.e. adds pixels to the

boundaries of objects in an image. We use dilation to fill the

any gaps between the road pixels so that we can perform the

next step efficiently.

2. Non road area removal - In this step we use the concept

that road regions are an elongated area that has largest

connected component and the building and other non road

has relatively lesser area and is not connected to road.

This step involves-

1. Find the connected components.2. Compute the area of each component.

3. Remove non road parts (small objects like building,

parking lots and other similar objects ).

D. Result evaluation:

The basic method of result evaluation is to compare the

results against a manually plotted, high quality reference





model, such as the one shown in figure 6.

The method for evaluating the results of road extraction

can be done by completeness, correctness and quality measure

evaluation. The quality of output depends on how much part

of our derived output matches with the reference data.

1. Completeness and correctness

The accuracy of any road extraction technique can be

summarized by measuring the completeness and correctness

of the derived road network. In order to evaluate the results

the quality measures completeness, correctness and quality as

defined in (Heipke et al., 1997) [12] are examined. The

classification of road network is done as true positive (TP),

false negative (FN), or false positive (FP).

Figure 6 Reference image

As Harvey [13] described, first a buffer of predefined width

is constructed around the reference road data (Figure 7). The

parts of the extracted data lying in the buffer are considered

as matched. The matched extracted data are denoted as true

positive with length TP, the unmatched extracted data is

denoted as false positive with length FP.

Second, matching is performed the other way round. The

buffer is now constructed around the extracted data (Figure

8), and the parts of the reference data within the buffer are

considered as matched. In this case of low redundancy their

length can be approximated with TP. The unmatched

reference data are denoted as false negative with length FN.

2. Quality measures

Quality ( Heipke et al.,1997) [12] focuses on the two main

point: (1) How complete is the extracted road, and (2) How

correct the extracted road. The completeness corresponds to

the user’s demands (“what is missing in the output I want”),

whereas the correctness is related to the probability of an

extracted linear piece to be indeed a road.

Completeness

Completeness = Length of matched reference

Length of reference

≈ TP

TP + FN

Completeness ϵ [0; 1]

Figure 7. Matched extraction ( Heipke et al.,1997) [12]

Figure 8. Matched reference ( Heipke et al.,1997) [12]

The completeness is the percentage of the reference data

which is explained by the extracted data, i.e., the percentageof the reference data which lies within the buffer around the

extracted data.

The optimum value for completeness is 1.

Correctness

Correctness = Length of matched extraction

Length of extraction





≈ TP

TP + FP

Correctness ϵ [0; 1]

The correctness represents the percentage of correctly

extracted road data, i.e., the percentage of the extracted data

which lie within the buffer around the reference data.

The optimum value for correctness is 1.

Quality

Quality = Length of matched extraction

qq

≈ TP

TP +FP+ FN

Quality ϵ [0; 1]

qq = length of extracted data + length of unmatchedreference

The quality is a measure of the “goodness” of the final result.

It takes into account the completeness of the extracted data

well as its correctness.

The optimum value for quality is 1.

We have tested the various images for road extraction, the

result of ten test images in terms of completeness, correctness

and quality is given in the following table.

Image Completeness Correctness Quality

1 0.934682 0.79289 0.751222

2 0.912124 0.683685 0.641435

3 0.884832 0.789277 0.715748

4 0.783529 0.772095 0.636354

5 0.734900 0.848870 0.649871

6 0.621824 0.570012 0.423276

7 0.580309 0.722598 0.479582

8 0.608722 0.723162 0.493681

9 0.595211 0.346973 0.28073010 0.677801 0.666831 0.506331

Table 1. Evaluation results

III. CONCLUSION

This method of road extraction can extract roads very

rapidly and gives very satisfactory results. But urban area

with large number of complicated buildings and area with

darker road needs to be researched further.

R EFERENCES

[1] Hedman, K.; Stilla, U.; Lisini, G.; Gamba, P.; , "Road Network Extractionin VHR SAR Images of Urban and Suburban Areas by Means of Class-

Aided Feature-Level Fusion," Geoscience and Remote Sensing, IEEETransactions on , vol.48, no.3, pp.1294-1296, March 2010.

[2] Hui Kong; Audibert, J.-Y.; Ponce, J.; , "General Road Detection From a

Single Image," Image Processing, IEEE Transactions on , vol.19, no.8,

pp.2211-2220, Aug. 2010.[3] P.N ANIL;Dr. S. NATARAJAN;," Automatic Road Extraction from High

Resolution Imagery Based On Statistical Region Merging andSkeletonization" International Journal of Engineering Science and

Technology Vol. 2(3), 2010, 165-171.[4] Yinghua He; Hong Wang; Bo Zhang; , "Color based road detection in

urban traffic scenes," Intelligent Transportation Systems, 2003.Proceedings. 2003 IEEE , vol.1, no., pp. 730- 735 vol.1, 12-15 Oct. 2003.

[5] Tomoko Tateyama, Zensho Nakao, Xian Yan Zeng, Yen-Wei Chen,"Segmentation of High Resolution Satellite Images by Direction andMorphological Filters," his, pp.482-487, Fourth International Conferenceon Hybrid Intelligent Systems (HIS'04), 2004.

[6] Tuncer, O.; , "Fully Automatic Road Network Extraction from Satellite

Images," Recent Advances in Space Technologies, 2007. RAST '07. 3rdInternational Conference on , vol., no., pp.708-714, 14-16 June 2007.

[7] H. Mayer, I. Laptev, A. Baumgartner, and C. Steger, “Automatic roadextraction based on multi-scale modeling, context, and snakes”, IEEE

Trans. on Med. Img., vol. 32, pp. 47–56, 1997.[8] Zhang, Q. and Couloigner, I., 2006a. Automated Road Network

Extraction from High Resolution Multi-Spectral Imagery. In: ASPRS 2006Annual Conference, Reno, Nevada, 10 pages.

[9] Nock, R.; Nielsen, F.; , "Statistical region merging," Pattern Analysis andMachine Intelligence, IEEE Transactions on , vol.26, no.11, pp.1452-1458, Nov. 2004.

[10] Jalal, A. A Fuzzy Model for Road Identification in Satellite Images.Proceedings of the 2006 International Conference on Image Processing,

Computer Vision, & Pattern Recognition, Las Vegas, Nevada, USA, 2006.[11] Anil Z Chitade, Dr. S.K. Katiyar, “Color Based Image Segmentation

using K-Means Clustering”, International Journal of Engineering Scienceand Technology Vol. 2(10), 2010, 5319-5325

[12] C. Heipke, H. Mayer, C. Wiedemann, and O. Jamet. Evaluation of automatic road extraction. In International Archives of Photogrammetryand Remote Sensing, volume 323-4W2, pages 151-160, 1997.

[13] Harvey, W.A., 1999. Performance evaluation for road extraction.

Bull. Soc. Franc Photogrammet. Tele detection 153 (1999-1), 79–87.

[14] Suman Tatiraju, Avi Mehta “Image Segmentation using kmeans clustering

, EM and Normalized Cuts”, University Of California Irvine . .





Test image 1 Output Reference image






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28.IJAEST Vol No 5 Issue No 2 Raod Extraction Using k Means Clustering and Morphological Operations 290 295