  The high dimensionality of hyperspectral images (HSI) that contains more than
hundred bands (images) for the same region called Ground Truth Map, often
imposes a heavy computational burden for image processing and complicates the
learning process. In fact, the removal of irrelevant, noisy and redundant bands
helps increase the classification accuracy. Band selection filter based on
"Mutual Information" is a common technique for dimensionality reduction. In
this paper, a categorization of dimensionality reduction methods according to
the evaluation process is presented. Moreover, a new filter approach based on
three variables mutual information is developed in order to measure band
correlation for classification, it considers not only bands relevance but also
bands interaction. The proposed approach is compared to a reproduced filter
algorithm based on mutual information. Experimental results on HSI AVIRIS
92AV3C have shown that the proposed approach is very competitive, effective and
outperforms the reproduced filter strategy performance.
  Keywords - Hyperspectral images, Classification, band Selection, Three
variables Mutual Information, information gain.

Computer Vision and Pattern Recognition