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Abstract

This letter introduces a nonlinear measure of independence
between random variables for remote sensing supervised
feature selection. The so-called Hilbert–Schmidt independence
criterion (HSIC) is a kernel method for evaluating statistical
dependence and it is based on computing the Hilbert–Schmidt
norm of the cross-covariance operator of mapped samples in the
corresponding Hilbert spaces. The HSIC empirical estimator is
easy to compute and has good theoretical and practical properties.
Rather than using this estimate for maximizing the dependence
between the selected features and the class labels, we propose
the more sensitive criterion of minimizing the associated HSIC
p-value. Results in multispectral, hyperspectral, and SAR data
feature selection for classification show the good performance of
the proposed approach.