A Remote Sensing Image Segmentation Method Based On Spectral and Texture Information

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1 Volume-5, Issue-6, December-2015 International Journal of Engineering and Management Research Page Number: A Remote Sensing Image Segmentation Method Based On Spectral and Texture Information P. Rajyalakshmi P.G. Student, Department of ECE, Sri Sai College of Engineering and Technology, ANANTAPUR, Andhra Pradesh, INDIA ABSTRACT Segmentation is an important problem in remote sensing image processing.in this paper, we propose a new method for segmenting a remote sensing image that provides spectral and texture information. Laplacian of gaussian (LoG) filters are used for the removal of noise. The enhanced image uses K-Mean clustering algorithm. Local spectral histogram representation, which comprises of histograms of filter responses in a local window, provides an effective feature to capture both spectral and texture information. The SVD is calculated for error estimation depending on the size of the image. The experimental results discussed in this paper provides MATLAB implementations of gray scale image, LoG filter, K-Mean algorithm, histogram equalization, and SVD with graph plot. Keywords Image Segmentation, Laplacian of gaussian (LoG) filters. Local spectral histogram, SVD singular value decomposition, MATLAB I. INTRODUCTION An input image after converting in to grey scale image is filtered by Laplacian of Gaussian (LoG) filters which is then is enhanced by histogram equalization. The enhanced image is then clustered by using K-mean clustering algorithm. Remote sensing images are segmented based on spectral and texture information. The SVD for error estimation is calculated based on the image size. Image segmentation has been extensively studied. In remote sensing, the spectral and spatial resolution capability in data acquisition depends on segmentation methods. Multispectral (MS) images, obtained from remote sensing radiometers, provide much improved capabilities for describing ground objects. In the interim, high-resolution images contain rich texture information, which provide improved segmentation results. Therefore, remote sensing segmentation methods are projected to make use of both spectral and texture information. It is well known that it is very difficult to predict visual texture information. In remote sensing image analysis, biological transformations are often employed to deal with texture information. It is difficult to define complex textures as the morphological operations have limited forms and hence it lacks the ability. Semivariograms, which measure spatial correlation, are recurrently used for texture analysis in geospatial data. Due to the high computational cost semivariograms used as texture descriptors becomes impractical for large images. Recent work on texture analysis illustrates an evolving consensus of an image which is ought to be first combined with a bank of filters which is then tuned to various orientations and spatial frequencies. For studying the local distribution of filter responses using texture descriptors have been shown to be powerful features for texture synthesis and refinement. To produce segmentation with texture descriptors a uniquely combined spectraltexture segmentation structure can be developed by providing integrated features to clustering methods. On the other hand, there are two main problems related with such framework. First, high-dimensional features are generated for using multiple filters to spectral bands. As a consequence, not only various clustering methods turn out to be unsuccessful, but the computational cost is also increased. The second problem roots difficulty in localizing region boundaries due to the texture descriptors that are produced from the image windows crossing multiple regions. One needs to choose a set of filters to specify spectral histograms. Generally there are three types of filters namely, the intensity filter, Laplacian of Gaussian (LoG) filters, and Gabor filters. Local spectral histogram 351 Copyright Vandana Publications. All Rights Reserved.

2 defined as, histogram of filter responses in a local window is used. This illustration provides an effective feature to capture both spectral and texture information. Nevertheless, local spectral histograms as a form of texture descriptors, also suffer from the problems of high dimensionality and boundary localization. To recover these problems, we work on a newly proposed segmentation method, which frames segmentation as multivariate linear regression. This method works across different bands produces accurate results for boundary localization in a computationally efficient way. II. LITERATURE SURVEY. X.Liu,D.L.Wang recommended Image and texture segmentation using local spectral histogram for segmenting images comprising of texture and nontexture areas based on local spectral histograms. ; Local spectral histograms are Well-defined as a vector containing of marginal distributions of selected filter responses which offer a feature measurement for both kinds of regions. By means of local spectral histograms of identical regions, we subdivide the segmentation method into three phases. The initial classification is the first phase, probability models for similar texture and non-texture regions are computed and an initial segmentation result is achieved by categorizing local windows. The second phase provides an algorithm based on the derived probability models, which repeatedly update the segmentation. In the third phase boundary localization is performed, where region boundaries are confined to a small area by constructing enhanced probability models that are sensitive to spatial patterns in segmented areas. We present segmentation results on texture as well as non-texture images. It achieves high accuracy. The boundary smoothness is affected. F.Porikli, employed A fast way to extract histograms in Cartesian spaces to calculate the histograms for every probable target regions in a Cartesian data space. This technique provides three different advantages: 1) Compared to conventional approach, this approach is computationally efficient as it uses the integral histogram technique which can perform extensive search process in real-time, which was impossible before. 2) It can be stretched to higher data dimensions, uniform and non-uniform bin formations, and multiple target scales devoid of losing its computational advantages. 3) It facilitates the description of higher level histogram structures. It achieves the spatial procedure of data points, and iteratively transmits an accumulated histogram by starting from the origin and traversing through the remaining points along either a scan-line or a wave-front. At every segment, it updates a distinct bin using the values of integral histogram at the formerly visited neighbouring data points. After the integral histogram is transmitted, A Remote Sensing Image Segmentation Method Based On Spectral And Texture Information histogram of any target region can be calculated straightforwardly by using simple mathematical operations. Using simple arithmetic operations, Histogram of every target region can be calculated easily. It is more sensitive in certain environments. B.Johnson, Z.Xie, approached Unsupervised image segmentation evaluation and refinement using a multi-scale approach which is used to develop the segmentation of a high spatial resolution (30 cm) color infrared image of a suburban area. Initially, a sequence of 25 image segmentations is performed in Definiens. Professional 5 using different scale parameters. The optimal image segmentation is recognized using an unsupervised valuation technique of segmentation feature that takes into account global intra-segment and intersegment heterogeneity measures (weighted variance and Moran s I, respectively). After the optimal segmentation is determined, under-segmented and over-segmented regions in this segmentation are recognized using local heterogeneity measures (variance and Local Moran s I). The under-segmented and over-segmented regions are developed by (1) further segmenting under-segmented regions at finer scales, and (2) merging over-segmented regions with spectrally similar neighbours. This procedure leads to the formation of numerous segmentations consisting of segments produced at three different segmentation scales. Evaluation of single- and multi-scale segmentations illustrates that, distinguishing and refining under-segmented and over-segmented regions using local statistics can progress global segmentation results. It can progress global segmentation results. It is applied with more complicated algorithm to the system. J.Yuvan, D.L.Wang, implemented segmentation using local spectral histograms and linear regression Local spectral histograms is feature A Remote Sensing Image Segmentation Method Based On Spectral And Texture Information vectors consisting of histograms of chosen filter responses, which capture both texture and non-texture information. Based on the observation that the local spectral histogram of a pixel location can be approximated through a linear combination of the representative features weighted by the area coverage of each feature, we formulate the segmentation problem as a multivariate linear regression, where the solution is obtained by least squares estimation. Moreover, we propose an algorithm to automatically identify representative features corresponding to different homogeneous regions, and show that the number of 352 Copyright Vandana Publications. All Rights Reserved.

3 representative features can be determined by examining the effective rank of a feature matrix. It can produce segmentation with high accuracy and great efficiency. It produces imprecise boundaries for multiple regions. J.Malik, S.Belongie, developed Contour and texture analysis for image segmentation It delivers a procedure for segregating gray-scale images into disjoint regions of coherent brightness and texture. Normal images cover both textured and untextured regions, so the signals of contour and texture variances are oppressed instantaneously. Contours are treated in the intervening contour framework, while texture is evaluated using textons. Each of these signals has an area of applicability, so to simplify cue combination, we present a gating operator based on the texturedness of the neighbourhood at a pixel. Having obtained a local measure of how likely two nearby pixels are to belong to the same region, we use the spectral graph theoretic framework of normalized cuts to find partitions of the image into regions of coherent texture and brightness. The whole performance and accuracy is good. It will minimize the number of edges found in the texture area and contrast is weak. III. EXISTING SYSTEM Satellite images are automatically segmented which is useful for obtaining more timely and accurate information. Segmentation of an image is realized as partitioning the image into sub regions with similar attributes. For geographical and spatial applications, high spatial resolution satellite imagery has become an important source of information. But, it does not make use of spatial information; the number of clusters cannot usually be obtained directly and automatically. The feature extraction is difficult and the process is more complicated. III. PROPOSED SYSTEM Remote sensing image is used as an input image for segmenting the image. First, the input image is converted into the gray image and then filtered by Laplacian of Gaussian (LoG) filters. Then the filtered image is enhanced by the histogram equalization. The image partitioned as cluster groups using K-mean algorithm. After clustering, image is segmented by the RGB colours. Based on the size of the image the SVD is calculated for error estimation. The overall performance is good. In Order to develop the project, we calculate combined spectral and texture information using local spectral histograms in which local histograms of all input bands are concatenated. IV. METHODOLGY SOFTWARE REQUIREMENTS: OS : Windows 7 Software : Mat lab R2013A HARDWARE REQUIREMENTS: Processor : Dual core. RAM : 2GB System architecture concentrates on the internal interfaces between system components and its external environment, especially the user.. Figure 1: Block diagram for each module Input Image: Remote sensing images are taken as input to the system and saved image in the computer is converted into the grey image. In order to improve the quality of the images we normally employ some filtering operations. Filtered Image: Then the gray scale image is filtered by using Laplacian of gaussian (LoG) filters. To specify the histogram there are certain set of filters. But in this process we use Laplacian of gaussian (LoG) filters. It is used for the removal of noise. Enhanced Image: The images are enhanced by using local spectral histogram. The histogram is taken for all input bands. It provide effective feature for both spectral and texture information for remote sensing images. Then the enhanced image is clustered. Clustered Image: K-mean clustering algorithm was used for clustering the image. Algorithm classifies or group the objects based on attributes/features into K number of group. Where, K is positive integer number. The grouping is performed by minimizing the sum of squares of distances between data and the corresponding cluster centroid. Segmented Image: Image segmentation is division or separation of image into region of similar attributes. The clustered image is then segmented based on the spectral and texture features. The images of segmented boundaries are represented with RGB colours. Performance: The SVD is calculated for error estimation based on the size of the image. The SVD is extensively used in image processing for plotting the graph. The overall performance is good. Algorithm 1 K-mean clustering method Given a set of observations (x 1, x 2,, x n ), in which each observation is a d-dimensional real vector, k-means clustering partitions the n observations into k ( n) sets 353 Copyright Vandana Publications. All Rights Reserved.

4 S = {S 1, S 2,, S k } in order to reduce the sum of squares within clusters. Where, μ i is the mean of points in S i. Segmentation by K-Means Algorithm The algorithm iterates over two steps: 1. Calculate the mean of each cluster. 2. Calculate the distance of each point from each cluster by computing its distance from the resultant cluster mean and allocate each point to the cluster which is nearest to it. Repeat the above two steps until sum of squared in group errors cannot be lowered any more. V. SIMULATION RESULTS AND DISCUSSION Figure 4: Filtered image Remote sensing image segmentation is done by using Matlab and a remote sensing image from the system is loaded (given as input) and the respective simulation results, SVD along with graph plot are shown below: Figure 5: A simulation result of enhanced image Figure 2: Remote sensing input image Figure 6: A simulation result for clustered image Figure 3: Gray-level image for an input image 354 Copyright Vandana Publications. All Rights Reserved.

5 Figure 7: A simulation result for segmented image In the above figure 7 the remote sensing image is segmented based on RGB colors. VI. CONCLUSION Figure 8: A simulation result for performance and accuracy percent The above figure 8 shows the percentage of accuracy achieved after segmentation. In this project we have employed a new technique for segmenting remote sensing images which uses local spectral histograms to provide combined spectral and texture features where each feature is regarded as a linear combination of several descriptive features, we frame the segmentation problem as an outcome of multiple correlated random variables as vectors which can be solved by least squares estimation. We have also proposed methods based on SVD to automatically evaluate descriptive features and select proper scales. The simulation results are encouraging REFERENCES [1] X. Liu and D. L. Wang, Image and texture segmentation using local spectral histograms, IEEE Trans. Image Process., vol. 15, no. 10, pp , Oct [2] F. Porikli, Integral histogram: A fast way to extract histograms in Cartesian spaces, in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2005, pp [3] B. Johnson and Z. Xie, Unsupervised image segmentation evaluation and refinement using a multiscale approach, ISPRS J. Photogramm. Remote Sens., vol. 66, no. 4, pp , Jul [4] J. Yuan, D. L. Wang, and R. Li, Image segmentation using local spectral histograms and linear regression, Pattern Recognit. Lett., vol. 33, no. 5, pp , Apr Copyright Vandana Publications. All Rights Reserved.

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