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IJCSI International Journal of Computer Science Issues Vol 11 Issue 3 No 1 May 2014. ISSN Print 1694 0814 ISSN Online 1694 0784,www IJCSI org 155. Existing solutions for segmentation of satellite images face. three major drawbacks It presentation degradation when. supplied with large sized images degradation of, segmentation accuracy due to the quality of the acquired. image and speed of segmentation is not meeting the. standards of the modern equipment s This paper considers. the use to ERDAS Imagery of preprocessing segmentation. techniques Preprocessing performs operations on the input. imagery to improve the imagery quality and FCM, clustering algorithm is to increase the image quality by the. segmentation process It includes Color transformation. intensity correction method and parameter selection edge. or boundary enhancement and de noising 2 Out of these. boundary enhancement pixel correction and de noising. a TopoSheet b Satellite Image, have more impact on segmented results Fig 1 Study Area. This paper is planned as follows Section 2 reviews the The Kiliar Sub Basin area around the Palar Basin is. related works in image segmentation Section 3 data set located at Latitude 12 41 9 N and 12 22 32 N and. used in study area Section 4 explains the proposed image Longitude 79 53 26 E and 79 25 10 E Studied images. segmentation method Section 5 presents the experimental of Kiliyar Sub basin is a Pan and Liss III merged data. results and the work is concluded in Section 6 panchromatic stereo pair of 5 86m pixel size and proper. radiometric quality a base to height ratio equal to taken. 2 Review of Literature on March 2013 Figure 1 b Viewer 1 shows the Study. z ulaikha et al Have proposed to improve spatial FCM area Satellite Terrain data IRS P6 Liss III and Figure 2 a. algorithm The histogram based FCM is used to initial the Viewer 2 shows the Georeferenced data Toposheet the. input parameters for ISFCM because HFCM coverage above data are collected from Remote Sensing Institute. more rapidly as it clustering the whole image Taramani Chennai. Kannan et at Have proposed a novel fuzzy clustering for Table 1 Methodological Specification of IRS P6 Liss III Imagery. intensity in homogeneities or weighted bias estimation and Image Type Pan and Liss III Merged Data. File Format Geo TIFF, segmented of medical images of same pattern The author.
Projection Type UTM,has presented a centre knowledge method. Spheroid Name WGS 84,Datum Name WGS84, Pawlak have describe discrepant uncertainties inherent in UTM Zone 1. satellite remote sensing imagery for geospatial features North or South North. classification can be taken care of by use of soft computing. technique effectively For the purpose rough sets fuzzy ERAS Image software is used to segment the exact area. set and rough fuzzy tie up ant colony optimization AOI by using the FCM Algorithm The current imagery. biogeography based optimization and particle swarm type and file format details are shown in Table 1 which. optimization methods are compared also presents the main characteristics of the acquired. 3 Study Area and Dataset Used images, Palar is a south Indian river originating from the 4 Image Segmentation Method. Nandidurg hills of Karnataka it flows through the states of Segmentation is a way to dividing raster image into. Karnataka 93 km Andhra Pradesh 33 km and Tamil segments based on pixel values and positions Pixels that. Nadu 222 km before finally draining into the Bay of are spatially connected and have similar values are. Bengal at Vayalur This river is divided in to 8 sub basins clustered in a single segment In ERDAS IMAGINE Image. This mostly covers Thiruvannamalai and Kanchipuram Segmentation performs edge detection on the raster image. districts an area of about 939 91km2 of which about It executes segmentations on that raster image using edges. 92 43 of the total area found in the edge detection phase as boundaries of. Copyright c 2014 International Journal of Computer Science Issues All Rights Reserved. IJCSI International Journal of Computer Science Issues Vol 11 Issue 3 No 1 May 2014. ISSN Print 1694 0814 ISSN Online 1694 0784,www IJCSI org 156. can be calculated by the equation Where L is, Input Imagery Luminance R is RED G is Green and B is Blue.
X L 0 2989 R 0 5870 G 0 1140 B, RGB is a color space originated from CRT or similar. Color Conversion display applications when it was convenient to describe. color as a combination of three colored rays red green. Edge Segmentation,4 2 Edge segmentation, ERDAS imaging Segmentation process involves several. Parameter for Locating steps The major step is to input image conversion to. particular feature space which depends on the clustering. techniques uses two types,Grouping the Segmented Parameters. Primary step involves the conversion of the input,image into L RGB color value attributes using. Segmented Result fuzzy c means clustering method,Secondary step involves the image conversion to.
Fig 2 Methodology Diagram for image segmentation,feature space with the selected fuzzy c means. clustering method,4 1 Color Conversion, Most remote sensing systems create arrays of numbers The above method paving the way for next segmentation. representing an area on the surface of the Earth The entire process input image conversion to feature space of. array is called an image or scene and the individual clustering Method. numbers are called pixels picture elements such as water. body wetland forest area etc the value of the pixel Image Smoothing The edge detection for the given. represents a measured quantity such as light intensity over imagery will be done smoothen the image using specific. a given range of wavelengths However it could also iteration The specific iteration will be selected for the. represent a higher level product such as topography or each image is the tool If the imagery is noisy the. chlorophyll concentration or almost anything Some active smoothing process will be applied of the noisy pixel in the. systems also provide the phase of the reflected radiation so process of edge detection. each pixel will contain a complex number Typical array. sizes with optimum pixels and system with multiple. Threshold The specific threshold is used in the edge. channels may require megabytes of storage per scene. detection by considering the pixel The specific threshold. Moreover a satellite can collect 50 of these frames on a will be given in tool The pixels value and the neighboring. single pass so the data sets can be enormous, pixels is bigger means the pixel value selected for. comparison will be considered as a candidate for edge. There are several established color models used in. pixel The threshold specified in the tool will depend on. computer graphics but the most common are the Gray. the value differences of neighboring pixels along the. Scale model RGB Red Green Blue model HIS Hue edges. Saturation Intensity model and CMYK Cyan Magenta,Yellow Black model for Remote Sensing Technology. used in digital image processing by Gonzalez and Woods. Minimal Length In edge detection process specific the. minimum acceptable length of the edge The acceptable. 2008 has presented a detailed explanation, length will be measured from the adjacent point of the.
RGB and L Color Transformation When Red imagery and if it is less than the acceptable length the. Green and Blue light are combined it forms white segment method will be dropped. As a result to reduce the computational complexity 4 3 Parameter for locating. the geo referenced data that exists in RGB color, In this selection is set to be additional parameters used in. model is converted into a gray scale image The, edge detection process There are minimal value difference. range of gray scale image from black to white values and variance factor. Copyright c 2014 International Journal of Computer Science Issues All Rights Reserved. IJCSI International Journal of Computer Science Issues Vol 11 Issue 3 No 1 May 2014. ISSN Print 1694 0814 ISSN Online 1694 0784,www IJCSI org 157. These indicate the strength of the association between that. The minimum value is used for neighboring data element and a particular cluster Fuzzy clustering is a. segment between minimal differences process of assigning these membership levels and then. using them to assign data elements to one or more clusters. The variation factors specify the important role, that shows variation in pixel value with in the The most significant part of this segmentation method is. same segment This segmented result plays in grant of feature value In the grant of feature value is based. defining whether expand the segment or not on simple idea that neighboring pixels have approximately. same values of lightness and chroma Then an actual. Area of interest parameter AOI is to use the specify the image noise is corrupting the imagery data or imagery. selected areas of the image to perform the Segmentation commonly contains of textured segments. Basic segmentation methods based on fuzzy c means, 4 4 FCM Algorithm clustering algorithm are working as follows.
The main aim of a clustering technique is to divide a set of. Algorithm Fuzzy C Means FCM, objects into a cluster which signifies subsets or a cluster. 3 Procedure Segmentation Image I No of Clusters c No of. The cluster divided in to two properties there are. homogeneity inside clusters and heterogeneity between Pre processing the image I. clusters Initialize cluster center v using the ordering split. The raw data belonging to a single cluster should repeat. be as similar as possible called Homogeneity Update partition matrix U. inside clusters Update prototypes matrix V,Until is a matrix norm. Regularize the partition U, The raw data which belongs to different clusters Return U V. should be as different as possible called End procedure. Heterogeneity between the clusters Fig 4 FCM Algorithm. Algorithm 1 Cluster Centers Initializations The FCM algorithm allots pixels to each class by using. Required X dataset C no of Clusters fuzzy memberships Let X x1 x2 xN denotes an. Procedure ordering split X c image with N pixels to be segregated into c clusters where. Compute m for each k k 1 n xi represents multispectral imagery features data The. Apply to m the ordering function algorithm is an iterative optimization that minimizes the. for i 0 to c do cost function defined as follows,li i n c N c. J u ijm x j vi,end for j 1 i 1, for i 1 to c do where uij represents the membership of pixel xj in the ith.
S i l i 1 1 l i cluster vi is the ith cluster center is a norm metric and m. Ci S i is a constant The parameter m controls the fuzziness of the. 1 resulting partition is used in this study, Step1 Choose a number of clusters in a given image. Step2 Assign randomly to each point coefficients for being in. End procedure,Step3 Repeat until convergence criterion is met. Fig 3 Cluster Centers Initialization,Step4 Compute the center of each cluster. Step5 For each point compute its coefficients of being in the. In hard or unsupervised clustering data is divided into cluster. distinct clusters where each data element belongs to. exactly one cluster In fuzzy clustering data elements can. belong to more than one cluster by using Algorithm 1 and The first measures of evaluation of segmentation were. associated with each element is a set of membership levels subjective and the ever growing interest in this topic. leaded to numerous metrics allowing proper evaluation In. Copyright c 2014 International Journal of Computer Science Issues All Rights Reserved. IJCSI International Journal of Computer Science Issues Vol 11 Issue 3 No 1 May 2014. ISSN Print 1694 0814 ISSN Online 1694 0784,www IJCSI org 158. order to objectively measure the quality of the,segmentations produced evaluation measures are.
considered in this paper,5 Experimental Results, The satellite images retrieved from various places have. been tested in our study area by using ERDAS IMAGING. software The results are summarized below, The figure1 gathered from the satellite is given an input to. the FCM algorithm where the image undergoes various. transformations Forest Wetland Water Body and River c River Basin original image. areas are the four different regions selected from the. satellite imagery using AOI tools, The following figure 5 a Forest figure 5 b Wetland. figure 5 c Water Body figure 5 d River are the,preferred regions. The satellite imagery does not reveal the clear picture of. the selected regions and so the above four figures a b. c d are distinguished from figure1 to make the image. more visible,d Forest original image,Fig 5 Selected Images from figure1.
The FCM Algorithm takes as input the above images and. segments the images according to the regions with,minimum distance. The following images when passed through the FCM,algorithm using ERDAS IMAGING software get. transformed in to the following images as figure 6 a. figure 6 b figure 6 c and figure 6 d respectively The. places that are recognized from the scalable imagery using. the FCM method generate the segmented results of the. a Wetland original image,selected regions,b Water Body original image. a Wetland original image, Copyright c 2014 International Journal of Computer Science Issues All Rights Reserved. IJCSI International Journal of Computer Science Issues Vol 11 Issue 3 No 1 May 2014. ISSN Print 1694 0814 ISSN Online 1694 0784,www IJCSI org 159.
pixels are rarely to make practical and effective use of. References, 1 Sonka Milan Vaclav Hlavac and Roger Boyle Image processing. analysis and machine vision Vol 3 Toronto Thomson 2008. 2 J Horvath Image Segmentation Using Clustering Kosice 2003. P No 88 Master thesis, 3 Krasteva Rumiana Bulgarian Hand Printed Character. Recognition Using Fuzzy C Means Clustering Bulgarian. Academy of sciences problems of engineering cybernetics and. robotics 53 2002 112 117, b Water Body original image 4 Marroquin Jose L and Federico Girosi Some extensions of the k. means algorithm for image segmentation and pattern. classification No AI M 1390 Massachusetts Inst of Tech. Cambridge Artificial Intelligence Lab 1993, 5 Zhang Dao Qiang and Song Can Chen A novel kernelized fuzzy. c means algorithm with application in medical image. segmentation artificial intelligence in medicine 32 no 1 2004. 6 Mitra Pabitra B Uma Shankar and Sankar K Pal Segmentation. of multispectral remote sensing images using active support vector. machines Pattern recognition letters 25 no 9 2004 1067 1074. 7 Pawlak Zdzislaw Rough set theory and its applications to data. analysis Cybernetics Systems 29 no 7 1998 661 688, 8 Zadeh Lotfi A Fuzzy sets Information and control 8 no 3.
1965 338 353, 9 Chitade Anil Z and S K Katiyar Colour based image. segmentation using k means clustering International Journal of. c River Basin original image Engineering Science and Technology 2 no 10 2010 5319 5325. 10 Naz Samina Hammad Majeed and Humayun Irshad Image. segmentation using fuzzy clustering A survey In Emerging. Technologies ICET 2010 6th International Conference on pp. 181 186 IEEE 2010, 11 Still Susanne William Bialek and L on Bottou Geometric. Clustering Using the Information Bottleneck Method In NIPS. 12 Cheng Jian Lan Li Bo Luo Shuai Wang and Haijun Liu High. resolution remote sensing image segmentation based on improved. RIU LBP and SRM EURASIP Journal on Wireless,Communications and Networking 2013 no 1 2013 1 12. 13 Darwish Ahmed Kristin Leukert and Wolfgang Reinhardt Image. segmentation for the purpose of object based classification. In International Geoscience and Remote Sensing Symposium vol. 3 pp III 2039 2003, d Forest original image 14 MacDonald Darren Jochen Lang and Michael McAllister. Evaluation of colour image segmentation hierarchies. Fig 6 Segmented Images Target Region In Computer and Robot Vision 2006 The 3rd Canadian. Conference on pp 27 27 IEEE 2006, 15 Chen H C and S J Wang Visible colour difference based.
quantitative evaluation of colour segmentation In Vision Image. 6 Conclusion and Signal Processing IEE Proceedings vol 153 no 5 pp 598. 609 IET 2006, FCM clustering is a hard and an unsupervised clustering. 16 Devaux Jean Christophe Pierre Gouton and Fr d ric Truchetet. technique which will be applied to image segments to Aerial color image segmentation by Karhunen Loeve transform. clusters with spectral properties FCM use the distance In Pattern Recognition International Conference on vol 1 pp. between pixels and cluster centers in the spectral domain 1309 1309 IEEE Computer Society 2000. to compute the membership function Image pixels are 17 Tang Jun A color image segmentation algorithm based on region. growing InComputer Engineering and Technology ICCET. highly correlated and this spatial information is an 2010 2nd International Conference on vol 6 pp V6 634 IEEE. important characteristic that can be used to aid their 2010. classification However the spatia67l relationship between 18 Nadir Kurnaz Mehmet Z mray Dokur and Tamer lmez. Segmentation of remote sensing images by incremental neural. Copyright c 2014 International Journal of Computer Science Issues All Rights Reserved. IJCSI International Journal of Computer Science Issues Vol 11 Issue 3 No 1 May 2014. ISSN Print 1694 0814 ISSN Online 1694 0784,www IJCSI org 160. network Pattern Recognition Letters 26 no 8 2005 1096. 19 Bartneck N and W Ritter Colour segmentation with polynomial. classification In Pattern Recognition 1992 Vol II Conference. B Pattern Recognition Methodology and Systems Proceedings. 11th IAPR International Conference on pp 635 638 IEEE 1992. 20 Meng Lingkui Jun Fang Shurong Lou and Wen Zhang Study. on Image Segment Based Land Use Classification and Mapping. In Information Engineering and Computer Science 2009 ICIECS. 2009 International Conference on pp 1 4 IEEE 2009, 21 Pal Nikhil R and Sankar K Pal A review on image. segmentation techniques Pattern recognition 26 no 9 1993. Copyright c 2014 International Journal of Computer Science Issues All Rights Reserved.

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