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Classification of remotely sensed images using the gene sis fuzzy segmentation algorithm

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Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training......

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Classification of remotely sensed images using the gene sis fuzzy segmentation algorithm

  1. 1. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CLASSIFICATION OF REMOTELY SENSED IMAGES USING THE GENESIS FUZZY SEGMENTATION ALGORITHM By A PROJECT REPORT Submitted to the Department of electronics &communication Engineering in the FACULTY OF ENGINEERING & TECHNOLOGY In partial fulfillment of the requirements for the award of the degree Of MASTER OF TECHNOLOGY IN ELECTRONICS &COMMUNICATION ENGINEERING APRIL 2016
  2. 2. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CERTIFICATE Certified that this project report titled “Classification of Remotely Sensed Images Using the GeneSIS Fuzzy Segmentation Algorithm” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate. Signature of the Guide Signature of the H.O.D Name Name
  3. 3. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com DECLARATION I hereby declare that the project work entitled “Classification of Remotely SensedImages Using the GeneSIS Fuzzy Segmentation Algorithm” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER OF APPLIED ELECTRONICS is a record of original work done by me the guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any other thesis or work on the basis of which a degree or award was conferred on an earlier occasion to me or any other candidate. (Student Name) (Reg.No) Place: Date:
  4. 4. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ACKNOWLEDGEMENT I am extremely glad to present my project “Classification of Remotely SensedImages Using the GeneSIS Fuzzy Segmentation Algorithm” which is a part of my curriculum of third semester Master of Science in Computer science. I take this opportunity to express my sincere gratitude to those who helped me in bringing out this project work. I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.), PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project. I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from my deep heart for her valuable comments I received through my project. I wish to express my deep sense of gratitude to my guide Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for successful completion of this project. I also express my sincere thanks to the all the staff members of Computer science for their kind advice. And last, but not the least, I express my deep gratitude to my parents and friends for their encouragement and support throughout the project.
  5. 5. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ABSTRACT: In this paper, we propose an integrated framework of the recently proposed Genetic Sequential Image Segmentation (GeneSIS) algorithm. GeneSIS segments the image in an iterative manner, whereby at each iteration, a single object is extracted via a genetic algorithm-based object extraction method. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between fuzzy coverage, consistency and smoothness. GeneSIS exhibits a number of interesting properties, such as reduced over-/undersegmentation, adaptive search scale, and region-based search. To enhance the capabilities of GeneSIS, we incorporate here several improvements of our initial proposal. On one hand, two modifications are introduced pertaining to the object extraction algorithm. Specifically, we consider a more flexible representation of the structural elements used for the object’s extraction. Further more, in view of its importance, the consistency criterion is redefined, thus providing a better handling of the ambiguous areas of the image. On the other hand we incorporate three tools properly devised, according to the fuzzy principles characterizing GeneSIS. First, we develop a marker selection strategy that creates reliable markers, particularly when dealing with ambiguous components of the image. Furthermore, using GeneSIS as the essential part, we consider a generalized experimental setup embracing two different classification schemes for remote sensing images: the spectral-spatial classification and the supervised segmentation methods. Finally, exploiting the inherent property of GeneSIS to produce multiple segmentations, we propose a segmentation fusion scheme. The effectiveness of the proposed methodology is validated after thorough experimentation on four data sets.
  6. 6. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com INTRODUCTION: Inrecent years, the growing development and availability of satellite imagery with high spectral-spatial resolution (HSSR) poses new challenges in the field of land cover classi- fication. An attractive method, having recently received considerable attention, is to incorporate spatial information in order to improve the classification results obtained by traditional pixelbased classifiers. One way to achieve this goal is to extract spatial features from fixed-size neighborhoods around pixels and combine them with the spectral bands in a single feature vector . These methods, however, raise the issue of scale selection, since they rely on fixed windows that are not sufficient to identify structures of different sizes existing in the image. To address this issue, Huang et al. proposed a framework for the automatic window selection for every pixel and the fusion of this multiscale information. In an edge extraction technique is used to initially partition the image into boundary and nonboundary pixels. These two sets are classified separately, and the resulting maps are subject to various geometric operators before the final fusion step is carried out. Finally, some other approaches incorporate spatial information into the SVM classifier, either by modifying its decision function and constraints or by using composite kernels A more effective alternative for integrating spatial information is to perform image segmentation. Segmentation is the partitioning of the image into disjoint regions so that each region is connected and homogeneous with respect to some homogeneity criteria of interest. According to Fu and Mui most of the segmentation methods can be divided into three categories: edge-based, clustering/feature thresholding and region-based. Edge-based methods operate on the spatial space, searching for discontinuities in the image by examining the existence of local edges. The extracted edges finally enclose the created objects. Watershed transformation is the most commonly used method of this category, having been extensively employed in various remote sensing studies Derivaux et al. propose a supervised segmentation method, where watershed is applied to a transformed feature space of fuzzy membership values. The optimal segmentation is finally obtained via a genetic algorithm optimization of the watershed parameters. Li et al. employ a marker-based watershed algorithm with embedded edge information. All these approaches suffer from a major limitation, i.e., sensitivity to local spectral variations, which typically results in oversegmentation of the image
  7. 7. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com On the other hand, clustering techniques operate in the spectral space, searching for significant modes in the patterns distribution. The created clusters are then mapped back to the spatial domain in order to form the segmentation map. Tarabalka et al. follow this strategy by employing the ISODATA and EM clustering algorithms. The generated segmentation map is finally combined with the SVM classification results via majority voting, to produce the final spectral-spatial classification. A similar classification scheme is proposed in with the difference that a weighted majority voting rule is used. An important demerit of the aforementioned methods is the ignorance of the spatial association of pixels during the clustering process. A method coping with this issue is presented in where the mean-shift density-estimation technique is used for data clustering in a joint spectral-spatial space Region-based methods rely on the assumption that adjacent pixels in the same region have similar spectral features, and hence, most likely belong to the same class. Some methods in this group exploit the graph representation of the image and perform segmentation by utilizing graph theory-based algorithms. Specifically, in the minimum spanning forest (MSF) constructed in each tree is rooted on a classification derived marker, whereas in the graph-cut algorithm is employed for solving the metric labeling problem. However, region growing is the most commonly employed methodology in this domain. Region growing segmentation algorithms usually start from a pixel level and evaluate a homogeneity criterion in order to decide which neighboring pixels and/or objects should be merged next. The process is repeated sequentially, until a termination condition is satisfied. Fractal net evolution approach (FNEA) is one of the most known methods of this category, which tries to minimize the objects inner heterogeneity. The applied heterogeneity criterion utilizes both spectral and shape information of the objects, by considering two different shape components, namely compactness and smoothness. Hierarchical step-wise optimization (HSWO) evaluates a dissimilarity function between all neighboring objects and the merging decision is taken via the best merge rationale: the pair with the smallest dissimilarity is the one to be merged. Tilton extended this method by allowing constrained merges of spatially nonadjacent regions. In all the aforementioned methods, proper selection of the termination conditions has always been a challenging task. The definition of a meaningful stopping criterion. However, straightforward. So, instead of having a single segmentation, they take advantage of the hierarchy existing in the continuous merges and create multiple ones by stopping the
  8. 8. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com merging process at different phases. The end result is a hierarchy of segmentations, each with a different scale, from coarser to finer ones. The demerit here is that a range of thresholds, usually with no physical meaning, must be chosen and the resulting hierarchy must be examined by an expert in order to obtain the most suitable segmentation. Recently, several methods have been proposed to confront with this problem, attempting to automatically obtain a single segmentation map from a hierarchy . Specifically, the Classification and Hierarchical Optimization (CaHO) and the Hierarchical Segmentation with integrated Classification (HSwC) achieve this goal by incorporating knowledge from a supervised pixel-based classifier in the employed dissimilarity criterion. In Marker-based HSEG (M-HSEG) markers are extracted initially from an SVM map. During the merging process, a restriction is imposed, where regions with different marker labels cannot be merged. Furthermore, in a binary partition tree structure is used to store the hierarchical region-merging segmentation. The final segmentation is obtained by tree pruning, according to a properly defined criterion. Various segmentation methods have been suggested in the past that make use of evolutionary algorithms, and particularly genetic algorithms (GA). GAs are universal optimization methods, inspired from the genetic adaptation of natural evolution . Some methods use the GA to optimize a set of parameters that control a common segmentation algorithm . In this case, each chromosome encodes a different set of parameters, thus an entire image segmentation is completed for each chromosome’s evaluation. The simplicity in representation is contrasted here to the high computational load. Another category includes those methods adopting the global encoding approach, where each chromosome encodes a segmentation of the whole image . The representation here increases the search space complexity of the GA exponentially. Therefore, optimal solutions can only be attained at the expense of large population sizes and after a large number of generations. Genetic Sequential Image Segmentation (GeneSIS) is a recently proposed algorithm for object-based classification of remotely sensed images. It segments the image sequentially, which is a single object is extracted at a time via a GA-based object extraction algorithm (OEA). This allows the adoption of a simpler solution encoding, which reduces the search space complexity considerably. In this paper, we present an integrated framework of GeneSIS that incorporates several enhancements compared with our initial proposal. The synergetic contribution of these
  9. 9. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com additions improves significantly both the classification accuracy and the image description qualities of GeneSIS. First, we introduce two constructive modifications involved in OEA with the goal to increase the flexibility of the chromosome solutions and facilitate a better representation of the significant ground structures existing in the terrain. Specifically, OEA evolves a population of structuring elements placed on the image, called the basic search frames (BSFs), which are represented by rectangular windows of varying size. BSFs are continuously relocated over the generations, trying to find the best object for extraction. Objects are extracted as connected subsets from the interior of BSFs. A drawback of GeneSIS is that owing to the axis-aligned encoding of BSFs, it produces occasionally strong oversegmentation results when dealing with images containing rotated ground truth structures. To address this problem, we adopt in this paper a more general representation of solutions, by incorporating rotation of the BSFs. Moreover, each candidate object of the population is evaluated in terms of three fuzzy fitness criteria: coverage, consistency and smoothness. In particular the consistency criterion used to measure the homogeneity property plays a key role in the segmentation process. For this reason, we redefine consistency making GeneSIS capable of handling the ambiguous areas of the image more effectively. In addition to the aforementioned algorithmic improvements, we also incorporate three further extensions involved in different parts of our classification configuration, based on existing approaches of the literature. All these tools are properly formulated, adapting to the fuzzy representation principles characterizing GeneSIS. First, since GeneSIS is a markerdriven algorithm, we suggest a marker selection strategy to generate more reliable markers, in an attempt to enhance the segmentation process. The method concentrates on the core (confident) regions of the ground components. Markers are selected according to the components size and the fuzziness of the contained pixels. A noteworthy distinction of our approach compared with existing schemes is that the pixels uncertainty is measured here by considering the difference of fuzzy degrees between the dominant and the most competing classes. Secondly, in the experimental analysis, we investigate two GeneSIS-based classification schemes, namely the supervised segmentation and the spectral- spatial classification methods. In the former case, GeneSIS is applied to fuzzy images acquired via supervised SVM classification. In the latter case, the initial maps are obtained after fuzzy clustering. Instead of considering these maps as the final segmentation results as in, here we apply
  10. 10. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com GeneSIS to the unsupervised fuzzy maps before spectral-spatial combination. Owing to the different usage of the combination strategy, we are able to obtain more accurate classification results. Finally, the third extension aims at incorporating in GeneSIS the fusion principles from multiple segmentation/classification maps. This is an effective technique having attracted considerable attention recently in remote sensing. For instance, a multiscale segmentation framework is developed in by embedding nonlinear scale-space filtering, whereas suggests a SVM ensemble model that combines multiple spectral/spatial features at both pixel and object levels. In this context, we exploit the inherent property of GeneSIS to produce multiple segmentations emanating from different randomizations. Then, we propose a segmentation fusion approach, where an ensemble of multiple classifications maps is combined via a fuzzy majority voting rule. The aggregation scheme also incorporates the certainty degrees to the various classes of extracted segments in the different segmentations. Experimental evaluation indicates that fusion assures at least best accuracy values from the ensemble, whereas on the other hand, it improves the quality of classification maps. The rest of this paper is organized as follows. In Section II, we provide a general description of the proposed scheme, whereas Section III focuses on the GeneSIS approach. The OEA algorithm is detailed in Section IV, whereas Section V presents the segmentation fusion scheme. The University of Pavia image is used as a test bench in Section VI to display the segmentation results obtained by the proposed scheme and illustrate its properties. Experimental results on three other images are presented in Section VII. This paper concludes in Section VIII with some final remarks.
  11. 11. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CONCLUSION: The newly developed GeneSIS segmentation algorithm has been investigated in this paper for classification of remotely sensed images. The new framework incorporates significant additions, including the rotation of BSFs, the marker selection strategy, refinement of the consistency criterion, supervised/ unsupervised segmentation and segmentation fusion. Comparative analysis on four data sets demonstrated that GeneSIS is favorably contrasted against other segmentation methods of the literature. As a future research, we are considering various issues to improve GeneSIS further. An observed drawback of the proposed method is the inadequacy in delineating smoothly more complicated object boundaries. To this end, we are examining several techniques to confront with this demerit, namely, other powerful representations of BSFs such as polygonal shapes, the consideration of fine objects as the structural units in segmentation, and more effective approaches to delineate the active areas of BSFs. These enhancements preserve the encoding simplicity, but on the other hand allow the creation of more flexible and smooth objects.
  12. 12. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com REFERENCES: [1] X. Huang and L. Zhang, “An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 12, pp. 4173–4185, Dec. 2008. [2] Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, “Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers,” IEEE Trans. Syst., Man, Cybern., vol. 40, no. 5, pp. 1267–1279, Oct. 2010. [3] J. Bai, S. Xiang, and C. Pan, “A graph-based classification method for hyperspectral images,” IEEE Trans. Geosci. Remote Sens., vol. 51, no. 2, pp. 803–817, Feb. 2013. [4] Y. Tarabalka, J. C. Tilton, J. A. Benediktsson, and J. Chanussot, “A marker-based approach for the automated selection of a single segmentation from a hierarchical set of image segmentations,” IEEE J. Sel. Topics Appl. Earth Observ., vol. 5, no. 1, pp. 262–272, Feb. 2012. [5] J. C. Tilton, Y. Tarabalka, P. M. Montesano, and E. Gofman, “Bestmerge region growing segmentation with integrated nonadjacent region object aggregation,” IEEE Trans. Geosci. Remote Sens., vol. 50, no. 11, pp. 4454–4467, Nov. 2012. [6] Y. Tarabalka and J. C. Tilton, “Spectral-spatial classification of hyperspectral images using hierarchical optimization,” in Proc. Workshop Hyperspectral Image Signal Process.: Evolution Remote Sens., 2011, pp. 1–4. [7] Y. Tarabalka and J. C. Tilton, “Best merge region growing with integrated probabilistic classification for hyperspectral imagery,” in Proc. IEEE Int. Geosci. Remote Sens. Symp., 2011, pp. 3724–3727.

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