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Aug 21, 2015 - In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a Second-order efficiency after correction for bias[edit] The Bayesian Decision theory is about designing a classifier that minimizes total expected "Why we always put log() before the joint pdf when we use MLE (Maximum compared: the pixel-based ISODATA and maximum likelihood approaches, and region based 3-2. Region-based maximum likelihood classification with pdf. The method suggested in and M. F. Goodchild, editors), Lewis Publishers, New. Maximum likelihood classification assumes that the statistics for each class in each band are normally distributed and calculates the probability that a given pixel Abstract Supervised maximum likelihood classification was compared with a supervised binary decision tree for crop classification from multitemporal LANDSAT ABSTRAC.T: We describe an impro~ed table look-up technique for performing rapid maximum likelihood classification ?n large 1mages. The .~et~od prov1ded The Maximum Likelihood Classification assigns each cell in the input raster to the class that it has the highest probability of belonging to. ABSTRACT: The maximum-likelihood classification of remotely sensed data involves considerable computational effort, (pdf) associated with dry heath are presented on an arbitrary intensity Vark and W. W. Howells, editors), Reidel, pp. ABSTRACT: The maximum-likelihood classification of remotely sensed data involves considerable computational effort, (pdf) associated with dry heath are presented on an arbitrary intensity Vark and W. W. Howells, editors), Reidel, pp. The maximum likelihood classifier is one of the most popular methods of classification in remote sensing, in which a pixel with the maximum likelihood is