A novel approach to the selection of robust and invariant features for classification of hyperspectral images

This paper presents a novel approach to feature selection for the classification of hyperspectral images. The proposed approach aims at selecting a subset of the original set of features that exhibits two main properties: i) high capability to discriminate among the considered classes, ii) high invariance (stationarity) in the spatial domain of the investigated scene. The feature selection is accomplished by defining a multiobjective criterion that considers two terms: i) a term that assesses the class separability, ii) a term that evaluates the spatial invariance of the selected features. The multi-objective problem is solved by an evolutionary algorithm that estimates the Pareto-optimal solutions. Experiments carried out on a hyperspectral image acquired by the Hyperion sensor confirmed the effectiveness of the proposed technique. © 2008 IEEE.