Kernel adaptive subspace detector for hyperspectral target detection
In this paper, we present a kernel-based nonlinear version of the adaptive subspace detector (ASD) that detects signals of interest in a high dimensional (possibly infinite) feature space associated with a certain nonlinear mapping. In order to address the high dimensionality of the feature space, ASD is first implicitly formulated in the feature space which is then converted into an expression in terms of kernel functions via the kernel trick of the Mercer kernels. The proposed kernel-based ASD (KASD) exploits the nonlinear correlations between the spectral bands that is ignored by the conventional ASD. Experimental results based on the given hyperspectral image show that the proposed KASD outperforms the conventional ASD.