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In this study a method has been proposed by using of the local information of image pixels in outlier identification to reduce the time of image classification. The applied algorithm is Expectation Maximization (EM algorithm) which is an iterative algorithm. In this algorithm, in each step, outliers are detected then removed in order to prevent error propagation in next steps. By decreasing the errors in each step the validation and accuracy of the classification is increased. Thus we proposed a method which use from the mean entropy of pixels which are in neighborhood of first, second and third edge pixels of mixture to the image. By using of this method the time of classification of a typical image (AVIRIS hyperspectral image) has been improved.

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