Classification of Remote Sensing Images from Urban Areas Using of Image laplacian and Bayesian Theory

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1 Classification of Remote Sensing Images from Urban Areas Using of Image laplacian and Bayesian Theory B.Yousefi, S. M. Mirhassani, H. Marvi Shahrood University of Technology, Electrical Engineering Faculty 7 Th Tir Square, P.o.Box , Shahrood,IRAN bardiyausefi@gmail.com, mostafamirhassani@gmail.com, h.marvi@shahroodut.ac.ir ABSTRACT This paper presents the methodology of urban area classification in high resolution satellite IKONOS imagery. The strategies include building extraction by Bayesian theory and laplacian criterion, labeling and size filtering, intensity threshold and etc which are applied to IKONOS image in tandem to make this algorithm as an effective strategy to save processing time and improve robustness. To realize the strategy, First, vegetation are extracted in attend to green layer of RGB image then buildings are detected by Bayesian decision theory in regard to laplacian probability density function, then shadows which have low intensity are detected. In the next step a special intensity level is calculated as a threshold level to discern roads. Finally open areas are extracted from remained of image as regions with low laplacian intensity and large size. Meanwhile morphological operations are applied to remove redundant image's particles. Experimental result indicates that this approach has a high efficiency especially in extraction of large roads and streets from dense urban area IKNOS images. Keywords: Urban area, classification, Laplacian, Bayesian theory 1. INTRODUCTION In view of very high resolution commercial satellite images availability,being both spectral and spatial kind of these image and extraction of useful data from these images attract many scientific research by using this images data of small structures in urban areas such as roundabouts an small buildings is possible. These identified structures can be applied for urban development planning, emergency response, or earth survey. Recently, many of authors have proposed various methods for classification of under remote sensing images. Many of classification algorithms use tandem feature extraction step. In [1][2][3] the morphological profile in accompany with image contrast are used to provide feature vectors. A classifier using neural network presented in [4] which detect structure according to their geometrical characteristics. Also in [5] same features are used to a classifier based on possibility models for structure classification. Some authors proposed a multi stage classifier [6] in which a fuzzy and a maximum likelihood based classifier are applied. On road detection in aerial images an excellent survey [7] presented by Mayer et al. Nayao et al. in [8] introduced a system which detects buildings and road networks simultaneously. In [9], [10] images laplacian be used as the edge detector to detector to determine the building boundaries. in this paper we employ the image laplacian as a criterion to extract buildings and open area from image meanwhile morphological operation such as opening and closing be applied to eliminate redundant particles and reach a more accurate result. The novelty of this paper thus comes from: 1. The Bayesian discriminating analysis of the input image that is presented in 2.b improves the building extraction accuracy; 2. Using of typical differential morphological profiles (DMP) obtained for various pixels, [1], is time consumer. The purposed method increases the rate of classification up to 40% with decreasing computational cost; 3. Experimental results show streets extraction in dense urban areas, racketing by this purposed method; Optomechatronic Computer-Vision Systems II, edited by Jonathan Kofman, Yuri Lopez de Meneses, Shun'ichi Kaneko, Claudio A. Perez, Didier Coquin, Proc. of SPIE Vol. 6718, 67180F, (2007) X/07/$18 doi: / Proc. of SPIE Vol F-1

2 The rest of this paper organized as follow. In section 2, a review of laplacian as criterion which indicates the variation of image intensity introduced,. The techniques for identifying building, streets, highways.shadows and open areas are described in section 3.exprimental results are presented in section 4, followed by conclusion in section METHODOLOGY According to figure1, solution of urban area classification problem depends on way of considering these images. In the first sight some suitable criterions should have been determined to make discrimination between different image s classes effectively.figure1 shows an urban area IKONOS image. It is obvious that classes consist of large and small buildings, shadows, open areas, highways and streets which are manually classified. Figure1. An Urban area's photo 2.1. Laplacian and Edge detection Laplacian is ones of the most important features which indicate the intensity variation of image. In laplacian image we can see some information about each part of main image intensity variations, smooth and rough surfaces and etc. laplacian could be calculated with this formula: v2{ Fr.tyfl = -:rri + Where F(x,y) is main image. Figure 2 shows image laplacian.in two dimensional images, edges are considered as linear features in which the magnitude of the first derivative along the gradient direction is maximum. Figure 2: image laplacian. Proc. of SPIE Vol F-2

3 In two dimensional images, edges are considered as linear features. This is equivalent to taking the zero-crossings of the second derivative but numerically more reliable. There are many methods for edge detection such as Sobel, Prewitt, Robert, Canny and etc.we used Canny edge detection to obtain high frequency map of IKNOS image. The Canny algorithm[13] included the following important steps: _ The image is blurred with a two-dimensional smoothing kernel to reduce the affects of noise. _ The intensity gradient, magnitude & orientation of the gradient direction are detected in the smoothed image f(x; y). They can be obtained as below: The gradient, In the Canny edge detector, features are located over local maxima in the magnitude of the gradient vector, It can be shown, again by the use of error propagation, that this calculation will give rise to a spatially uniform noise distribution and is therefore a suitable basis for a statistically stable feature enhancement. The gradient direction is the direction in the image for which the gradient changes more rapidly. It can be calculated as follows in case of a step edge: O=tan' [ Non-maximal suppression is carried out in the direction perpendicular to the estimated orientation so as to retain only the edges with maximum gradient values. _ Adaptive threshold with hysteretic is performed to eliminate noise & streaking of edge contours. This makes provisions for retaining true edges with actuating intensity values. The Canny edge detector is based on optimizing three important criteria that are essential in the process of edge detection. _ Good Detection: The probability should be low for a) not detecting real edges b) detecting false edges. _ Good localization: The detected edges should be near to the centre of the true edges. _ Good response: There should be only one true response to a single edge. I 2.2 morphological operations Morphological image processing is a type of processing in which the spatial form or structures of objects within an image are modified. Dilation, erosion, and skeletonization are three fundamental morphological operations. With dilation, an object grows uniformly in spatial extent, whereas with erosion an object shrinks uniformly. Opening is an erosion operation, followed by dilation. Meanwhile the structure element (or SE) determines growing and shrinking rate of objects. The effect of morphological opening by reconstruction is to remove all structures in the image that are both smaller than the SE and brighter than their surroundings. Proc. of SPIE Vol F-3

4 2.3 Building extraction As it's shown in laplacian image, building s laplacian are restricted to specific rates. These values are relatively large in edges and are very small in ceilings while in other parts of image, laplacian value is different. In figure 3 the probability density function of image according to its laplacian is depicted. is laplacian density variation. i.l. JJa. Figure3:In PDF chart, vertical axe is Laplacian intensity and horizontal axe is P ( ). The blue, red and gray colors represent open areas P (1 ), buildings P (2 ) and open areas P (3 ) respectively. Figure3 shows the class-conditional probability density function which indicates the probability density of mentioned classes respect to as laplacian intensity of according image s pixels. P Represents the probability of 1 Which refers to laplacian intensity level and 1 refers to building class.in the same way, 2 3 refers to open areas and roads class. Each PDF has been provided from IKONOS images database. 1 1 if P( 1 ) P( 2 ) 2 otherwise It is obvious from figure 3 that by considering >0.5 P 1 P 2. First building's particles can be extracted by considering >0.5 in laplacian spectrum which mostly contains buildings edge. Other part of buildings spectrums which have lower amount of laplacian are still mixed in original image. This part of laplacian spectrum exists in open area and buildings image. These two classes can be discriminated by considering size criterion.to this aim first these parts of images have been labeled. As open areas are larger than buildings, each part of image that has at least certain size is member of open area class. So the regions which are dependent on open areas have been eliminated from building map. In next step with a same method, small buildings will be specified from large buildings. 2.4 Shadows extraction Shadows and vegetations have lowest intensity in image (see figure1).as previously mentioned, vegetation regions have been eliminated from original image according to its green layer (from RGB) consequently in figure 4 only the shadows have lowest intensity. Figure 4 shows the shadows which have been yielded using a threshold level in original image.(suppose that maximum level of image intensity is 1) Proc. of SPIE Vol F-4

5 Figure4: shadows with 0.2 as threshold 2.5 Extraction of highways, streets and open areas Up to now shadows, buildings and vegetations have been obtained from image and are removed. The remaining regions are depicted in figure 5.a. To be certain that high frequency components, which don't exist in open areas and also in highways, have been removed an edge image, using of Canny method, have been subtracted from remained image so that the high frequency components are eradicated. See figure 5.b. Figure 5.a: input image without building and shadows Figure 5.b:figure5.a after Canny edges elimination As figure 5 depicts, streets extraction is more difficult than other classes such as open areas and highways. First reason is shadows elimination that leads to removing some parts of streets and second is thinness of streets compare with highways. However with considering the thinness characteristic, discriminating of street from other remained classes is possible. Proc. of SPIE Vol F-5

6 Figure 6: streets have been removed from Figure 5 As indicated in figure 6, street have been removed by opening operation from figure 5.so by subtracting this image from last, the streets can be indicated. Existence of automobiles and highway s edges and etc causes a special variation in highways intensity image. This intensity variation which doesn t exist in open areas could be discriminated by applying a threshold level in figure 5. Then by using of a Gaussian filter a grayscale image has been obtained. Particular intensity level of this gray image indicates highways. Figure 7: Highways extraction Figure 7 point to highway extraction. To calculating this threshold, following algorithm has been proposed: Let as default lowest threshold where i refers to stage and im is image of figure 5. IMT = im > i = i =i+1 Answer = i i dilate ( IMT) IMT i If i =Max ( ) Calculated value can discriminate highways from open areas. Figure 9 depicts the flowchart of this approach. 3. EXPERIMENTAL RESULTS In this section, the application of proposed method is presented. A square matrix with size of 3*3 as structure element(se) of opening operator have been utilized to remove the redundant particles of image in each step. For street extraction from highways 4*4(=16 pixels) square matrix as SE has been used. Proc. of SPIE Vol F-6

7 Figure 8: manual classified image (left) input image (middle) and classified image (right) Shadows Buildings Higinvays, Sir eets Large Buildings Small Buildings Op en areas Highways Figure 9: Flowchart of our proposed method The minimum size of label has been considered equal to 250 pixels to discriminating small buildings from large building also for taking out large building from open areas minimum size for label is 500pixels. Quantitative results obtained with proposed method are shown in Table I. TABLE I Classification Accuracies [3] Large Building% 47.4 Houses% 67.4 Highways% 43.7 The Proposed Method Proc. of SPIE Vol F-7

8 Streets% Open Areas% Shadows% Global% Fig. 8 presents the classification results obtained with this approach (algorithm presented in [II]). The following lookup table is used: shadows are in brown, large buildings in red, houses (small building) in gray, highways in greenish blue, streets in blue and open areas in green. 4. CONCLUSION A fully automated algorithm for the extraction of building, highways, open areas, shadows and etc from commercially available high resolution satellite imagery has been presented. After elimination of vegetations, Buildings were extracted from laplacian of grayscale image. For extraction of other classes three features are employed: image intensity, laplacian intensity, morphological operations such as opening and closing which represents the size of each object. Furthermore object's size has been calculated from labeled image. A review of canny edge detection and morphological operations was given. When applied to an IKONOS image test site the algorithm produced extraction of the buildings which have enough contrast with background. Shadows might cause error in extraction of streets by eliminating them. We plan to extend this approach by including the minimum risk in Bayesian decision rule. REFERENCES 1.M. Fauvel, Jocelyn Chanussot and Jon Atli Benediktsson "Classification of Remote Sensing Images From Urban Areas Using a Fuzzy Possibilistic Model "IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 3, NO. 1, JANUARY M. Fauvel, Jocelyn Chanussot and Jon Atli Benediktsson"Fusion of Methods for the Classification of Remote Sensing Images from Urban Areas"IEEE /05/ M. Fauvel, Jocelyn Chanussot, and Jón Atli Benediktsson "Decision Fusion for the Classification of Urban Remote Sensing Images" IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 44, NO. 10, OCTOBER J. A. Benediktsson, M. Pesaresi, and K. Arnason, Classification and feature extraction for remote sensing images from urban areas based on morphological transformations",ieee Transaction on Geosciences and Remote Sensing, vol. 41, no. 9, pp , September J. Chanussot, J. A. Benediktsson, and M. Vincent, Classification of remote sensing images from urban areas using a fuzzy model, in Geosciences and Remote Sensing symposium, vol. 1. IGARSS 04. Proceedings, September 2004, pp A. K. Shackelford and C. H. Davis, A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas", IEEE Transaction on Geosciences and Remote Sensing,vol. 41, no. 9, pp , September H. Mayer, A. Baumgartner, and C. Steger, Road extraction from aerial imagery, in CV-Online, M. Nagao, T. Matsuyama, and Y. Ikeda, Region extraction and shape analysis in aerial photographs" Computer Vision,Graphics and Image Processing, vol. 10, pp , Proc. of SPIE Vol F-8

9 9. Jie Tian, Jinfei Wang and Peijun Shi "Urban BUILDING BOUNDARY EXTRACTION FROM IKONOS IMAGERY" 10. S. M. Phalke and I. Couloigner "CHANGE DETECTION OF LINEAR MAN-MADE OBJECTS USING FEATURE EXTRACTION TECHNIQUE" 11 M. Pesaresi and J. A. Benediktsson, A new approach for the morphological segmentation of high resolution satellite imagery, IEEE Trans. Geosci. Remote Sens., vol. 39, no. 2, pp , Feb J. A. Benediktsson, M. Pesaresi, and K. Arnason, Classification and feature extraction for remote sensing images from urban areas based on morphological transformations, IEEE Trans. Geosci. Remote Sens., vol. 41, no. 9, pp , Sep J. Canny, A computational approach to edge detection, IEEE-PAMI, 8, , Proc. of SPIE Vol F-9