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authors Thales Sehn Korting - tkorting@dpi.inpe.br Luciano Vieira Dutra - dutra@dpi.inpe.br Guaraci José Erthal - ga

Assessment of a modified version of the EM algorithm for remote sensing data classification. E-step calculates the conditional expectation of the complete a posteriori probability function . M-step updates the parameter estimation (mean and covariance values). definition

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authors Thales Sehn Korting - tkorting@dpi.inpe.br Luciano Vieira Dutra - dutra@dpi.inpe.br Guaraci José Erthal - ga

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  1. Assessment of a modified version of the EM algorithm for remote sensing data classification E-step calculates the conditional expectation of the complete a posteriori probability function M-step updates the parameter estimation (mean and covariance values) definition TheExpectation-Maximization (EM) algorithm is a standard method to estimate Finite Mixture Models from observed data modifications K-means algorithm produces the first set of unknown parameters, when t = 0 A cluster is excluded when it presents low probability If a cluster center is approaching another center, one of them is randomly changed • results • Classification of a QuickBird scene, São José dos Campos, Brazil (R3G2B1) • Comparing Modified EM (70,58%), K-means (68,12%) and SOM (65%) • authors • Thales Sehn Korting - tkorting@dpi.inpe.br • Luciano Vieira Dutra - dutra@dpi.inpe.br • GuaraciJosé Erthal - gaia@dpi.inpe.br • Leila Maria Garcia Fonseca - leila@dpi.inpe.br

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