  Feature selection is one of the most important problems in hyperspectral
images classification. It consists to choose the most informative bands from
the entire set of input datasets and discard the noisy, redundant and
irrelevant ones. In this context, we propose a new wrapper method based on
normalized mutual information (NMI) and error probability (PE) using support
vector machine (SVM) to reduce the dimensionality of the used hyperspectral
images and increase the classification efficiency. The experiments have been
performed on two challenging hyperspectral benchmarks datasets captured by the
NASA's Airborne Visible/Infrared Imaging Spectrometer Sensor (AVIRIS). Several
metrics had been calculated to evaluate the performance of the proposed
algorithm. The obtained results prove that our method can increase the
classification performance and provide an accurate thematic map in comparison
with other reproduced algorithms. This method may be improved for more
classification efficiency. Keywords-Feature selection, hyperspectral images,
classification, wrapper, normalized mutual information, support vector machine.
