5-IJAEST-Volume-No-3-Issue-No-2-Automatic-Extraction-of-Road-Networks-115-121 by ISERP ISERP

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

Automatic Extraction of Road Networks based on Normalized cuts and Mean Shift method for high resolution Satellite Imagery M. Rajeswari*

K.S.Gurumurthy

Department of Telecommunication Engineering

Department of Electronics & Communication Engineering

Bangalore Institute of Technology

University College of Engineering,

Bangalore, 560 004, KA, India

Bangalore, 560 001 KA, India

E-mail: m.rajeswari@gmail.com

E-mail: drksgurumurthy@gmail.com

S.N. Omkar, J. Senthilnath

L. Pratap Reddy

Department of Aerospace Engineering

Department of Electronics & Communication Engineering

Indian Institute of Science

JNTU College of Engineering, Kukatpally Hyderabad-500 085 AP, India

E-mail: omkar@aero.iisc.ernet.in, snrj@aero.iisc.ernet.in

E-mail: pratap.fsf@gmail.com

Road extraction is difficult in the presence of context objects such as buildings or trees close to the road, disrupting the appearance of the road or occluding it. Baumgartner et al., [3] have considered context objects. For urban areas Zhang et al. [4] have developed their extraction based on multispectral classification and filtering using shape criteria. DSM from LIDAR is used as an additional data source by Hu, et al., [5] to restrict the search space for the straight lines. This cannot handle curved roads well. In region based extraction Doucette, et al., [6] used hyper spectral data channels to extract road regions and road pixels are grouped into a network with a kmedian classification. Hu, et al., [7] extracted road footprints based on their shape and then track them. Junction footprints are 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 road hypotheses is determined using fuzzy logic. Road network is generated using weighted graph and detour factor to close small and large gaps respectively. Poullis et al., [9] have extracted road using Gabor filter for image classification into road and non-road pixels segmentation using graph cut and Gabor filter for post processing

Abstractâ&#x20AC;&#x201D; To evaluate the speed of growth of an urban area road is one of the fast information updating element during urban development. 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 in urban region from high resolution satellite images.

Bangalore, 560 012, KA, India

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Keywords- Road extraction; Filtering; Mean shift; Normalized cut; performance evaluation

I. INTRODUCTION Analysis of high resolution satellite images has been an important research topic for accurate and up-to-date road network information essential for urban planning. Automated methods improve the speed and utility for road mapping and are therefore highly desirable. Fully automatic extraction of roads from satellite imagery does not require human intervention to perform time consuming and expensive process of mapping roads and has been an active research and development topic for the last twenty years. Many approaches for the automatic extraction of roads from satellite imagery are found in the literature. Zhang, [1] have done database verification and updating determining the region of interest for roads by a multispectral classification and excluding high regions using Digital Surface Models (DSM) then parallel edges are extracted in the regions of interest. For rural areas Mena et al., [2] have used three different classification methods for color and texture and are combined to extract road regions. Roads are only extracted in the regions around database roads.

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In literature on road extraction using mean shift Zhanfeng et al., [10] extracted segments from the image based on different scales, and are analyzed using transcendental knowledge. In Sun [11], the user inputs a set of nodes of the road and then the partitioning of the image is done using Mean Shift Method and roads are extracted between the nodes using Fast Marching Method. Yao-Yi [12] and Simler [13] applied mean shift as a filter to reduce the number of colors in the input image. Guo [14] applied mean shift to determine average saturation to classify the object. Saturation image is noisy and the road boundary is not recognizable. Hence post processing is used to extract roads. In literature on road extraction using Normalized cuts, Qihui et al., [15] have used normalized cuts for initial segmentation step also requires priori information like depth and intensity measurements from the range sensor for LIDAR

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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 remove small linear structures appearing as noise then the normalized cuts and Mean shift methods are used to extract the roads. The quality measures evaluated show that these approaches are accurate and robust for the extraction of roads. The proposed road network extraction process with Normalized cut and mean shift algorithms is described in section II. Section III describes the Performance evaluation. Section IV describes the experimental results with comparison and conclusions and future work are given in section V.

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 renders a 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. Median filtering is similar to using an averaging filter, in that each output pixel is set to an average of the pixel values in the neighborhood of the corresponding input pixel. However, with median filtering, the value of an output pixel is determined by the median of the neighborhood pixels, rather than the mean. The median is much less sensitive than the mean to extreme values (called outliers). Median filtering is therefore better able to remove these outliers without reducing the sharpness of the image. B. Segmentation for Road Extraction Image segmentation provides a powerful tool to extract features such as texture and shape from objects. Image segmentation is a process of partitioning the image into nonintersecting homogeneous regions on neighboring pixels, and no pairs of contiguous regions are homogeneous. In the proposed method for road extraction Segmentation methods are based on Normalized cuts and mean shift.

II. METHODOLOGY The proposed road extraction methodology has been broadly divided into two steps: the pre-processing and segmentation for road extraction.

such noise elements and homogeneous regions or segments are generated.

data and then extracts roads using hypothesis testing. Groth et al. [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 segments and are then evaluated using shape criteria to extract road parts.

Pre-processing Pre-processing is required to improve the image quality and to generate the elongated road network for further processing. Firstly, the panchromatic image was grouped into 15 clusters in an unsupervised classification. This was followed by a grouping operation to reduce the noise and smoothening of the image. This improves the tolerance as the noise is reduced and hence minimizes the effects of using a high resolution imagery (at such high-resolutions the road is susceptible to noise in the form of vehicular traffic, road markings, structural shadows, occlusions and many road surface contrast irregularities). Filtering operation generate the elongated road regions.

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1) Classification The images used are Quick-bird high resolution (2.4m and 0.6m) satellite images of the Bangalore urban area, India. The dimensions of the images are 1000X943 and 627X630 pixels. By setting the threshold value of 0.6, the high resolution image was grouped into 15 clusters in an unsupervised classification, five of which correspond to road networks. A level one classification was carried out by dividing the image into two classes: Roads and non-roads. 2) Grouping A nearest neighborhood grouping (NNG) operation was applied to the classified image for smoothening the spectral response within the pixelâ&#x20AC;&#x;s local neighborhood. In this process the vehicular occlusions and small patches of road contrast abnormalities were eliminated. In this method a pixel is chosen to begin with and its surrounding eight neighbors are considered for voting. If a class gets four or more votes, then the chosen pixel is assigned to that class. Otherwise the pixel retains its class. Road pixels in the neighborhood grow over

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1) Normalized cuts Method Normalized cuts given by Shi and Malik [19] is a graph based method using grouping of pixels maximizing similarity of pixels belonging to the same segment and minimizing similarity of pixels belonging to different segments. In optimal segmentation, an image is divided into two optimal segments and then recursively segmented. Ncut (normalized cut) measures the similarity between two segments. Nassoc (normalized association) measures similarity between pixels within a segment then maximizing Nassoc and minimizing Ncut. Normalized cut is an NP-complete problem so it is approximated using an Eigenvector equivalent problem. In normalized cuts, only the boundaries are considered. The advantage of this is that hard constraints are not needed to gain information about roads. This makes normalized cuts more conducive for automatic road extraction, but also limits the potential extraction that this method can produce because a user only has limited input in determining the extraction. The normalized cuts algorithm defines the optimal extraction in terms of an extraction that divides the image into two parts. To achieve a full extraction, initially an optimal binary extraction is performed and then recursively an optimal extraction within each 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 the similarity 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

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W  N  N symmetric matrix, where

 e W i, j     

 Fi  F j  F2

e 0

 X i  X j  X2

if j  N i  otherwise (1)

where Xi is the spatial location of i and Fi 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, only edges between two pixels that have a positive weight. To measure the similarity between sets, normalized cut is given by

cut ( X , Y ) cut ( X , Y )   assoc( X ,V ) assoc(Y ,V )

assoc(X, V) =

 w( p, q) 

pX ,qV

assoc(Y, V) 

 w( p, q) 

pY ,qV







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

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

 W (i, j) is the sum of costs from node i and j

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

y T D  W  y y T Dy T such that yi   1,b, 0  b  1, and y D1  0



min Ncut ( X , Y )  min y



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

D  W y  Dy

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

  D 2 D  W D 2 Z  Z 1

whereZ  D 2 y Steps of normalized cut algorithm are summarized as follows: 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 the second 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 the segment. 

Ncut(X,Y) =

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

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



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 converged Segmentation 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 unsupervised clustering. Clustering is a technique used to classify a large amount of data into different categories. It is unsupervised as there 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 drawn to 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 pixels in the image. The weighted average is called a kernel density estimator. Essentially, it is a function that, given a point x in the domain, weights all the points in the domain based on their distance to x. Kernel density estimation is based on Gaussian. By iteratively shifting the mean based on the kernel, all the pixels get drawn to a number of local points of       convergence. Segments are defined by grouping together

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1 n 1  fˆK x    d k  n i 1 h  i 

x  xi

   



with the bandwidth parameter hi > 0. The kernel K is a spherically symmetric kernel with bounded support satisfying [17],

  0

K x   c k , d k x

x  1



mG ( x )  C

ˆ f (x)  K  fˆ ( x )





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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  xi 2  i 1 xi g  h    m h ,G ( x)   x 2   x  x n i 1 g  h i    n



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

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 

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) = cg,d g(||x||2). The following property can be proven by taking the gradient of Equation (8) as follows,

2 xi  y j  xi  g i 1 h d  2   h i   j  1, 2,... (12) y j 1  2   n  y x 1  j i  g i 1 h d  2   h i   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.    mentioned  Look for the mode, in which the pixel (p) converges. This is carried out by using a uniform Gaussian kernel.  Assign in Zi(filtered image)the z component of the calculated value (intensity of the level grey).Run the mean shift filtering procedure for the image and store all the information about the d-dimension convergence point in Zi.  Define  the regions   spatial domain (hs) that are in the and that its intensities are smaller or equal than hr/2 (range domain).  For each of the defined regions in the previous step look 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 all adjacent 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. n

pixels whose mean converges to similar intensities. The movement of each mean to its point of convergence can be compared to a particle moving through a force field until it reaches an equilibrium state, or can be seen as the points moving toward peaks and valleys in a landscape. In this process there is clustering where the number of sets is determined by the image itself and there is no artificial initialization of sets. Given n data points x i, i=1,…,n in the d-dimensional space Rd, the kernel density estimation at the location x can be calculated by

III.

PERFORMANCE E VALUATION

The automatically extracted roads are compared with manually traced reference roads using ERDAS to perform accuracy assessment. Since roads have linear features, it is possible to use all the data rather than just sample points to conduct the accuracy assessment. In [21] several quality measures to evaluate the quality of extracted roads is proposed. The measures for accuracy assessment of road extraction are:      

Completene ss 

Correctnes s 

TP  TP  TN



TP  TP  FN



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TP  TP  FP  FN

 Re dundancy 

TP - [1 - FP  FN]  TP

RMSE 

 d (der i 1





 ref ) 2 



N=number of pieces of unmatched extraction

Number of Gaps/km =

n  Length of reference[ km]

n=number of gaps n

i 1





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 It filters 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 maximum    density location for the combined vector. The maximum density points are clustered and gradient ascent method is used for finding the local maximum of a target kerneldensity. 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 processing  wereapplied on  the segmented  image to extract road  steps segments. In this paper the normalized cuts algorithm is applied for the pre processed image to extract the road segments. As Normalized Cuts algorithm takes both local and global characteristics of the image are taken into account. Local characteristics are incorporated into the similarity matrix which considers the similarity of pixels in 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 and global aspects is a very important aspect of the Normalized Cuts algorithm. With this the algorithm ignores noise, small surface changes and weak edges. The results show that the extraction is successful as most segments cover only roads. Normalized cut uses spectral method and Mean Shift uses clustering method of tracking and both are iterative methods. For straight and long roads the boundaries are relatively easily obtained by both methods. Figure (2c) has several road segments and also contains a non road segment in the lower part of the left side of the image. Since the segments are defined by the image content and other 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.

 M ean gap length[m] =

 g (li )

Quick-bird image. Figure 1 shows: (a) the input image   (b) Preprocessed   image (c)  The extracted  1000X943; road network using normalized cuts with number of segments set 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 with number of segments set to 8 the segments (d) segmentation produced by mean shift with bandwidth =10 and minimum region=400. The experiments were conducted on Intel core2 duo CPU T5550 with 1.83 GHz windows XP system using Matlab 7.4.

 Quality 

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g(li) = length of the ith gap(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 is users‟ accuracy. The quality represents the overall accuracy Redundancy gives the percentage of correctly extracted roads which overlap as they correspond to the same 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 is not found. To calculate these quality measures, buffer zones are generated around the extracted roads and the reference roads. The chosen buffer width is approximately half of the actual road width. The direction difference is derived directly from the vector representations of both networks. A true positive (TP) is where the derived result coincides with the reference result. A false positive (FP) is where there is a road pixel in the derived result that is not in the reference data. A false negative (FN) is where there is a road pixel in the reference data that is not present in the derived result.

IV. EXPERIMENTAL RESULTS The images used are high resolution Quick-bird (0.6m and 2.4m) satellite images of the densely populated Bangalore urban area, India. The dimensions of the images are 1000X943 and 627X630 pixels. The algorithms were successfully tested and compared on the

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Figure 1: (a) Pan sharpened image 1000 X 943 (b) Preprocessed image (c) Normalized cut Extracted image (d) Mean Shift Extracted image

and the gap statistics. Manual extraction data was obtained using ERDAS tool. Table 1 shows the numeric results. The average completeness are from 80.69% to 96.09%, the average correctness are from 67.13% to 73.7%, the quality from 57.84% to68.87%and the average redundancy are from 1.432% to 1.728% for the satellite images . Figure (1d) shows an example result, where the geometry of the extracted 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-road features are also extracted redundancy is more because some of the road lines were extracted as shorter line segments with small orientation variation(1.728%) for high resolution image using mean shift .For low resolution image figure (2d)the mean shift produces results better than normalized cut results(96.09% completeness and 73.7% correctness). The average RMS difference and Mean gap length of mean shift are higher for low resolution images using mean shift compared to high resolution images. On the whole mean shift performs better than normalized cuts for 2.4m resolution image and for 0.6m resolution image normalized cut. CONCLUSIONS

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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 Quality Measure

Image1(1000X943) 0.6m Resolution Normalized Mean Cut Shift

Image 2(627X630) 2.4m Resolution Normalized Mean Cut Shift

Completeness%

96.09 70.86 68.87 1.451 3.978 0.763 0.04467

82.03 73.7 63.46 1.575 56.84 0.1934 0.472

Correctness% Quality%

Redundancy RMS (m)

Number of gaps per (km) Mean gap length (m)

80.69 67.13 57.84 1.728 18.8 5.34 0.0631

95.18 73.23 72.19 1.432 32.58 0.1934 0.14

For the extracted road network , the accuracy of the extraction results are based on the road extraction metrics proposed in [21], which include the completeness, correctness, quality, redundancy, root-mean-square (RMS)

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In this paper we have proposed two approaches to accurately extract the road networks. Pre-processing is used to eliminate the noise exploiting the structural information in the image and then normalized cut method and mean shift methods are applied leading to the extraction of elongated road segments. The main advantage of using normalized cut method is that both intergroup and intragroup characteristics of the image are taken into account. Intragroup characteristics are incorporated into the similarity matrix which considers the similarity of pixels in a close neighborhood. Intergroup characteristics come into play when the best cut is computed 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 assign each pixel the values of the nearest maximum density location for the combined vector. Then the maximum density points are clustered. The results obtained show robust modeling capability and better quality measures compared to earlier methods and are suited for automatic road extraction. Looking at the results shown in Table 1 we conclude that normalized cuts algorithm 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 and the numbers of segments have to be specified for each image. We are trying to estimate the appropriate number of segments from the given image. Normalized cut code handles images that have about 1, 00,000 pixels. For this, the input images were down sampled before the

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ACKNOWLEDGMENT This work was supported by the Space Technology Cell, Indian Institute of Science and Indian Space Research Organization grant. REFERENCES

[2] [3] [4] [5] [6] [7] [8]

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extraction. Also the images were converted to grayscale before the extraction. The Mean Shift method has radically symmetric kernels. Hence the change in the road width requires an adjustment of the kernel bandwidth to consistently track the road. Our future work includes overcoming these limitations

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ISSN: 2230-7818

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