  Band selection is a great challenging task in the classification of
hyperspectral remotely sensed images HSI. This is resulting from its high
spectral resolution, the many class outputs and the limited number of training
samples. For this purpose, this paper introduces a new filter approach for
dimension reduction and classification of hyperspectral images using
information theoretic (normalized mutual information) and support vector
machines SVM. This method consists to select a minimal subset of the most
informative and relevant bands from the input datasets for better
classification efficiency. We applied our proposed algorithm on two well-known
benchmark datasets gathered by the NASA's AVIRIS sensor over Indiana and
Salinas valley in USA. The experimental results were assessed based on
different evaluation metrics widely used in this area. The comparison with the
state of the art methods proves that our method could produce good performance
with reduced number of selected bands in a good timing.
  Keywords: Dimension reduction, Hyperspectral images, Band selection,
Normalized mutual information, Classification, Support vector machines
