Band Elimination for Dimensionality Reduction of Hyperspectral Images using Mutual Information

Hyperspectral sensors obtain a set of images from hundreds of contiguous and narrow bands of electromagnetic spectrum from visible to infrared regions. As large number of bands are present in these images, computational overhead for classifying them is very high. To speed up classification, reducing dimensionality by proper selection of a subset of bands is necessary. A filter based method using mutual information is proposed in this regard. Classification is done using Support Vector Machine classifier. Two evaluation measures, namely, overall classification accuracy and Kappa coefficient are considered to assess the efficiency of the proposed method. Performance of the proposed technique is compared with two other mutual information based methods and the proposed method is found to be better as compared to others.