  Hyperspectral images (HSI) classification is a high technical remote sensing
tool. The main goal is to classify the point of a region. The HIS contains more
than a hundred bidirectional measures, called bands (or simply images), of the
same region called Ground Truth Map (GT). Unfortunately, some bands contain
redundant information, others are affected by the noise, and the high
dimensionalities of features make the accuracy of classification lower. All
these bands can be important for some applications, but for the classification
a small subset of these is relevant. In this paper we use mutual information
(MI) to select the relevant bands; and the Normalized Mutual Information
coefficient to avoid and control redundant ones. This is a feature selection
scheme and a Filter strategy. We establish this study on HSI AVIRIS 92AV3C.
This is effectiveness, and fast scheme to control redundancy. Index Terms:
Hyperspectral images, Classification, Feature Selection, Normalized Mutual
Information, Redundancy.
