  Recently, the hyperspectral sensors have improved our ability to monitor the
earth surface with high spectral resolution. However, the high dimensionality
of spectral data brings challenges for the image processing. Consequently, the
dimensionality reduction is a necessary step in order to reduce the
computational complexity and increase the classification accuracy. In this
paper, we propose a new filter approach based on information gain for
dimensionality reduction and classification of hyperspectral images. A special
strategy based on hyperspectral bands selection is adopted to pick the most
informative bands and discard the irrelevant and noisy ones. The algorithm
evaluates the relevancy of the bands based on the information gain function
with the support vector machine classifier. The proposed method is compared
using two benchmark hyperspectral datasets (Indiana, Pavia) with three
competing methods. The comparison results showed that the information gain
filter approach outperforms the other methods on the tested datasets and could
significantly reduce the computation cost while improving the classification
accuracy. Keywords: Hyperspectral images; dimensionality reduction; information
gain; classification accuracy.
  Keywords: Hyperspectral images; dimensionality reduction; information gain;
classification accuracy.
