Adaptive Gaussian Mixture Estimation and Its Application to Unsupervised Classification of Remotely Sensed Images

منو

" Adaptive Gaussian Mixture Estimation and Its Application to Unsupervised Classification of Remotely Sensed Images "

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]