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Document Type : Latin Dissertation
Language of Document : English
Record Number : 148927
Doc. No : ET20719
Main Entry : SUMIT CHAKRAVARTY
Title Proper : Adaptive Gaussian Mixture Estimation and Its Application to Unsupervised Classification of Remotely Sensed Images
Note : This document is digital این مدرک بصورت الکترونیکی می باشد
Abstract : This master thesis addresses the unsupervised statistical classification to remotely sensedimages based on mixture estimation. The application of two well-known techniques,Expectation Maximization (EM).and Stochastic EM (SEM) algorithm to multi-dimensionalimage data is to be investigated, where Gaussian mixture is assumed. The initializationparameters are estimated by two different procedures namely the K-means algorithm andthe rough sets algorithm. Relative entropy is adopted as the criterion to measure the.
Subject : Electericl tess
: برق
electronic file name : TL43859.pdf
Title and statement of responsibility and : Adaptive Gaussian Mixture Estimation and Its Application to Unsupervised Classification of Remotely Sensed Images [Thesis]
 
 
 
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