Genetic algorithms and Linear Discriminant Analysis based dimensionality reduction for remotely sensed image analysis

Remotely sensed data (such as hyperspectral imagery) is typically associated with a large number of features, which makes classification challenging. Feature subset selection is an effective approach to alleviate the curse of dimensionality when the number of features contained in datasets is huge. Considering the merits of genetic algorithms (GA) in solving combinatorial problems, GA is becoming an increasingly popular tool for feature subset selection. Most algorithms presented in the literature using GA for feature subset selection use the training classification accuracy of a specific algorithm as the fitness function to optimize over the space of possible feature subsets. Such algorithms require a large amount of time to search for an optimal feature subset. In this paper, we will present a new approach called Genetic Algorithm based Linear Discriminant Analysis (GA-LDA) to extract features in which feature selection and feature extraction are performed simultaneously to alleviate over-dimensionality and result in a useful and robust feature space. Experimental results with classification tasks involving both hyperspectral imagery and SAR data indicate that GA-LDA can result in very low-dimensional feature subspaces yielding high classification accuracies.