Kernel spectral matched filter for hyperspectral target detection
In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery. The proposed spectral matched filter is defined in a kernel feature space which is equivalent to a nonlinear matched filter in the original input space. This nonlinear spectral matched filter is based on the notion that performing matched filtering in the high dimensional feature space increases the separability of spectral data mainly because it exploits the higher order correlation between the spectral bands. It is also shown that the nonlinear spectral matched filter can easily be implemented in terms of kernel functions using the so called kernel trick property of the Mercer kernels. The kernel version of the nonlinear spectral matched filter is implemented and simulation results on hyperspectral imagery are shown to outperform the linear version.