  In the feature classification domain, the choice of data affects widely the
results. The Hyperspectral image (HSI), is a set of more than a hundred
bidirectional measures (called bands), of the same region (called ground truth
map: GT). The HSI is modelized at a set of N vectors. So we have N features (or
attributes) expressing N vectors of measures for C substances (called classes).
The problematic is that it's pratically impossible to investgate all possible
subsets. So we must find K vectors among N, such as relevant and no redundant
ones; in order to classify substances. Here we introduce an algorithm based on
Normalized Mutual Information to select relevant and no redundant bands,
necessary to increase classification accuracy of HSI.
  Keywords: Feature Selection, Normalized Mutual information, Hyperspectral
images, Classification, Redundancy.
