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Variational Bayesian (VB) inference is the latest method for prediction of data or information in various processes. It provides a faster response with a reasonable accuracy as compared to the other methods (like Monte Carlo Markov Chain (MCMC) method). There is a large literature and work on prediction of data which deals with large amount of data. When data is missing, other methods, like MCMC,...
Accurate and precise statistical modeling of the radar data is an important problem. Probability density function (PDF) is a versatile statistical tool to provide functional description of the data. In this paper, a new PDF based approach is proposed to classify full polarimetric radar data into various land cover classes. Samples from each class are fitted with continuous domain PDFs to obtain statistical...
In this paper, an attempt has been made to improve information of an image by fusion technique, so it may be more effectively used. Full polarimetric images of ALOS PALSAR are fused by exploiting the nice properties of Expectation Maximization (EM) algorithm. Maximum likelihood classifier is applied on the composite unfused singlet and fused doublet images and results are compared on the basis of...
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