Recently, information extraction from hyperspectral images (HI) has become an attractive research area for many practical applications in earth observation due to the fact that HI provides valuable information with a huge number of spectral bands. In order to process such a huge amount of data in an eﬀective way, traditional methods may not fully provide a satisfactory performance because they do not mostly consider high dimensionality of the data which causes curse of dimensionality also known as Hughes phenomena. In case of supervised classiﬁcation, a poor generalization performance is achieved as a consequence resulting in availability of limited training samples. Therefore, advance methods accounting for the high dimensionality need to be developed in order to get a good generalization capability. In this work, a method of High Dimensional Model Representation (HDMR) was utilized for dimensionality reduction, and a novel feature selection method was introduced based on global sensitivity analysis. Several implementations were conducted with hyperspectral images in comparison to state-of-art feature selection algorithms in terms of classiﬁcation accuracy, and the results showed that the proposed method outperforms the other feature selection methods even with all considered classiﬁers, that are support vector machines, Bayes, and decision tree j48.