Kernel Adaptive Subspace Detector for Hyperspectral Imagery

Abstract

In this letter, we present a kernel-based nonlinear version of the adaptive subspace detector (ASD) that implicitly detects signals of interest in a high-dimensional (possibly infinite) feature space associated with a particular 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 property of the Mercer kernels. Experimental results based on simulated data and real hyperspectral imagery show that the proposed kernel-based ASD outperforms the conventional ASD and a nonlinear anomaly detector so called the kernel RX-algorithm.