28.IJAEST-Vol-No-5-Issue-No-2-Raod-Extraction-Using-k-Means-Clustering-and-Morphological-Operations- by ISERP ISERP

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

Road Extraction Using K-Means Clustering and Morphological Operations 

Index Terms— K-Means clustering, operations, road extraction, segmentation.

I. INTRODUCTION

morphological

road extraction from digital images has drawn a special attention in the last few decades. Numerous methods 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 is line 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

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 operations to 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.

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: Rohitmaurya87@gmail.com Email: shalinisingh@cdacnoida.in Email: poonam.r.gupta@gmail.com Email: Manisharma2009@gmail.com

ISSN: 2230-7818

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

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Extracted roads 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

(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. Input Image

Segmentation using K- Means clustering Road cluster identification

V = Σ Σ (xj - µi)2 i=1 xj ϵ Si

where there are k clusters Si, i = 1, 2, . . . , k. µi is the mean point of all the points xj 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) - µj || 2 j 5. Compute the new centroid or mean point for each clusters. m

Road cluster filtering Result evaluation ISSN: 2230-7818

µi :=

Σ 1{ c(i) = j } x(i) i=1

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

ÎŁ 1{ c(i) = j }

C. Road cluster filtering:

Where k is the number of clusters to be found, i iterates over the all the intensities, j iterates over all the centroids and Âľi are the centroid intensities.

B. Road cluster identification:

Figure 3. Segmented image with road cluster shown in red

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

i=1

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 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 involves1. 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:

Figure 4. Road cluster after segmentation

ISSN: 2230-7818

The basic method of result evaluation is to compare the results against a manually plotted, high quality reference

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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

Completeness Completeness = ≈

Length of matched reference Length of reference TP TP + FN

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).

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 ϵ [0; 1]

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

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.

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 percentage of 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

2. Quality measures ISSN: 2230-7818

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≈

TP TP + FP Correctness ϵ [0; 1]

REFERENCES

Quality Quality =

Length of matched extraction qq ≈

TP TP +FP+ FN

Quality ϵ [0; 1]

qq = length of extracted data + length of unmatched reference

Hedman, K.; Stilla, U.; Lisini, G.; Gamba, P.; , "Road Network Extraction in VHR SAR Images of Urban and Suburban Areas by Means of ClassAided Feature-Level Fusion," Geoscience and Remote Sensing, IEEE Transactions 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 and Skeletonization" 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 and Morphological Filters," his, pp.482-487, Fourth International Conference on 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. 3rd International Conference on , vol., no., pp.708-714, 14-16 June 2007. [7] H. Mayer, I. Laptev, A. Baumgartner, and C. Steger, “Automatic road extraction 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 2006 Annual Conference, Reno, Nevada, 10 pages. [9] Nock, R.; Nielsen, F.; , "Statistical region merging," Pattern Analysis and Machine Intelligence, IEEE Transactions on , vol.26, no.11, pp.14521458, 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 Science and 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 Photogrammetry and 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 . .

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.

[1]

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.

Completeness Correctness 0.934682 0.79289 0.912124 0.683685 0.884832 0.789277 0.783529 0.772095 0.734900 0.848870 0.621824 0.570012 0.580309 0.722598 0.608722 0.723162 0.595211 0.346973 0.677801 0.666831 Table 1. Evaluation results

Image 1 2 3 4 5 6 7 8 9 10

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.

Quality 0.751222 0.641435 0.715748 0.636354 0.649871 0.423276 0.479582 0.493681 0.280730 0.506331

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. ISSN: 2230-7818

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Output

Reference image

Output

Test image 5

Test image 1

Output

Reference image

Test image 6

Reference image

Test image 8

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Output

Reference image

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