  The Hyperspectral image (HSI) contains several hundred bands of the same
region called the Ground Truth (GT). The bands are taken in juxtaposed
frequencies, but some of them are noisily measured or contain no information.
For the classification, the selection of bands, affects significantly the
results of classification, in fact, using a subset of relevant bands, these
results can be better than those obtained using all bands, from which the need
to reduce the dimensionality of the HSI. In this paper, a categorization of
dimensionality reduction methods, according to the generation process, is
presented. Furthermore, we reproduce an algorithm based on mutual information
(MI) to reduce dimensionality by features selection and we introduce an
algorithm using mutual information and homogeneity. The two schemas are a
filter strategy. Finally, to validate this, we consider the case study AVIRIS
HSI 92AV3C.
  Keywords: Hyperspectrale images; classification; features selection; mutual
information; homogeneity
