A feature selection algorithm for class discrimination improvement

We propose a new feature selection algorithm for remote sensing image classification. Our approach has been especially devised for applications in which there is a large number of different features that can be potentially selected, implying that the search space is complex and high-dimensional. In this framework, our proposal is that of reformulating the feature selection problem as the search for the optimal subspace in which the different classes are more effectively discriminated. The search has been performed by using a genetic algorithm in which each individual encode the choice of a subspace, and its fitness is a measure of the class seperability in that subspace. The experimental results, performed on two databases, confirmed the effectiveness of the approach.