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In the feature classification domain, the choice of data affects widely theresults. For the Hyperspectral image, the bands dont all contain theinformation; some bands are irrelevant like those affected by variousatmospheric effects, see Figure.4, and decrease the classification accuracy.And there exist redundant bands to complicate the learning system and productincorrect prediction [14]. Even the bands contain enough information about thescene they may can't predict the classes correctly if the dimension of spaceimages, see Figure.3, is so large that needs many cases to detect therelationship between the bands and the scene (Hughes phenomenon) [10]. We canreduce the dimensionality of hyperspectral images by selecting only therelevant bands (feature selection or subset selection methodology), orextracting, from the original bands, new bands containing the maximalinformation about the classes, using any functions, logical or numerical(feature extraction methodology) [11][9]. Here we focus on the featureselection using mutual information. Hyperspectral images have three advantagesregarding the multispectral images [6],

Dimensionality Reduction and Classification Feature Using Mutual  Information Applied to Hyperspectral Images: A Wrapper Strategy Algorithm  Based on Minimizing the Error Probability Using the Inequality of Fano