Enhancing the performance of feature selection algorithms for classifying hyperspectral imagery

A method for enhancing the performance of feature selection algorithms is proposed. The proposed method is a two step process - first a feature subset is selected with optimum mutual information content and then this subset is searched to find a smaller subset, which has the best separability between classes. A subset with "optimum" mutual information content is the one which contains most of the information that is present in the rest of set. An expression has been derived to find such a subset efficiently. The two-step process is shown to reduce the search space drastically. The method is implemented with a simple genetic algorithm (SGA) and tested using hyperspectral remote-sensing images (acquired by AVIRIS sensor) as a data set. Theoretical result shows that the proposed method reduces the computation load by 90%. A computational efficiency to the order /spl sim/20% is obtained on the implementation of proposed method with SGA. The method is sufficiently general to be used to enhance other feature selection algorithms.