Relevant and invariant feature selection of hyperspectral images for domain generalization

This paper presents a novel feature selection method for the analysis of hyperspectral images. The proposed method aims at selecting a subset of the original features that are both 1) relevant for the considered problem (i.e., preserve the functional relationship between input and output variables), and 2) invariant (stable) across different domains (i.e., minimize the data set shift among different domains). Domains can be associated with images collected on different areas or on the same area at different times. We propose a novel measure of domain stability, which evaluates the distance of the conditional distributions between the source and target domain. Such a measure is defined on the basis of kernel embeddings of conditional distributions and can be applied to both classification and regression problems. Experimental results show the effectiveness of the proposed method in selecting features with high generalization capabilities on the target domain.