  The high dimensionality of hyperspectral images consisting of several bands
often imposes a big computational challenge for image processing. Therefore,
spectral band selection is an essential step for removing the irrelevant, noisy
and redundant bands. Consequently increasing the classification accuracy.
However, identification of useful bands from hundreds or even thousands of
related bands is a nontrivial task. This paper aims at identifying a small set
of highly discriminative bands, for improving computational speed and
prediction accuracy. Hence, we proposed a new strategy based on joint mutual
information to measure the statistical dependence and correlation between the
selected bands and evaluate the relative utility of each one to classification.
The proposed filter approach is compared to an effective reproduced filters
based on mutual information. Simulations results on the hyperpectral image HSI
AVIRIS 92AV3C using the SVM classifier have shown that the effective proposed
algorithm outperforms the reproduced filters strategy performance.
  Keywords-Hyperspectral images, Classification, band Selection, Joint Mutual
Information, dimensionality reduction ,correlation, SVM.
