  The profusion of spectral bands generated by the acquisition process of
hyperspectral images generally leads to high computational costs. Such
difficulties arise in particular with nonlinear unmixing methods, which are
naturally more complex than linear ones. This complexity, associated with the
high redundancy of information within the complete set of bands, make the
search of band selection algorithms relevant. With this work, we propose a band
selection strategy in reproducing kernel Hilbert spaces that allows to
drastically reduce the processing time required by nonlinear unmixing
techniques. Simulation results show a complexity reduction of two orders of
magnitude without compromising unmixing performance.
