1 June 2005 Kernel RX: a new nonlinear anomaly detector
Author Affiliations +
Abstract
In this paper we present a nonlinear version of the well-known anomaly detection method referred to as the RX-algorithm. Extending this algorithm to a feature space associated with the original input space via a certain nonlinear mapping function can provide a nonlinear version of the RX-algorithm. This nonlinear RX-algorithm, referred to as the kernel RX-algorithm, is basically intractable mainly due to the high dimensionality of the feature space produced by the non-linear mapping function. However, it is shown that the kernel RX-algorithm can easily be implemented by kernelizing it in terms of kernels which implicitly compute dot products in the nonlinear feature space. Improved performance of the kernel RX-algorithm over the conventional RX-algorithm is shown by testing hyperspectral imagery with military targets.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Heesung Kwon, Heesung Kwon, Nasser M. Nasrabadi, Nasser M. Nasrabadi, } "Kernel RX: a new nonlinear anomaly detector", Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.601834; https://doi.org/10.1117/12.601834
PROCEEDINGS
12 PAGES


SHARE
Back to Top