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Title: Empirical investigation of soft computing techniques in classification of remote sensed images
Researcher: Venkatalakshmi, K
Guide(s): Mercyshalinie, S
Keywords: Decision fusion technique
Genetic k Means Algorithm
Information and communication engineering
Particle swarm optimisation
Remote sensed images
Soft computing techniques
Upload Date: 14-Oct-2014
University: Anna University
Completed Date: 01/05/2009
Abstract: In this dissertation pattern recognition in remote sensed images is examined Initially classification using Neural Network classifier and statistical methods are discussed The classification results are combined using decision fusion technique Following this clustering algorithms are discussed for optimizing the cluster centers in images The proposed methods in this thesis are validated using Remote sensed images A new neural network classifier is proposed which is unsupervised and works well in terms of classification accuracies The decision of this classifier is combined with the results of maximum likelihood classifier and minimum distance to means classifier using Boolean logic This improves the classification accuracy Support vector machines also provide a good solution for the classification problem It finds an optimal linear hyperplane by which expected classification error for the test samples is minimized This support vector classifier when amalgamated with maximum likelihood classifier using majority voting technique produced 92 classification accuracy
Pagination: xviii, 112p.
Appears in Departments:Faculty of Information and Communication Engineering

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04_acknowledgement.pdf6.65 kBAdobe PDFView/Open
05_contents.pdf29.6 kBAdobe PDFView/Open
06_chapter1.pdf92.82 kBAdobe PDFView/Open
07_chapter2.pdf100.33 kBAdobe PDFView/Open
08_chapter3.pdf74.07 kBAdobe PDFView/Open
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10_chapter5.pdf135.71 kBAdobe PDFView/Open
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12_chapter7.pdf336.29 kBAdobe PDFView/Open
13_chapter8.pdf312.07 kBAdobe PDFView/Open
14_chapter9.pdf21.9 kBAdobe PDFView/Open
15_references.pdf40.36 kBAdobe PDFView/Open
16_publications.pdf8.96 kBAdobe PDFView/Open
17_vitae.pdf6.16 kBAdobe PDFView/Open

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