A Novel Approach to the Selection of Spatially Invariant Features for the Classification of Hyperspectral Images with Improved Generalization Capability

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 at the same time high capability to discriminate among the considered classes and high invariance in the spatial domain of the investigated scene. This approach results in a more robust classification system with improved generalization properties with respect to standard feature-selection methods. The feature selection is accomplished by defining a multiobjective criterion function made up of two terms: 1) a term that measures the class separability and 2) a term that evaluates the spatial invariance of the selected features. In order to assess the spatial invariance of the feature subset, we propose both a supervised method (which assumes that training samples acquired in two or more spatially disjoint areas are available) and a semisupervised method (which requires only a standard trai...