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