Kernel RX: a new nonlinear anomaly detector

Heesung Kwon; Nasser M. Nasrabadi

doi:10.1117/12.601834

1 June 2005 Kernel RX: a new nonlinear anomaly detector

Heesung Kwon, Nasser M. Nasrabadi

Proceedings Volume 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI; (2005) https://doi.org/10.1117/12.601834
Event: Defense and Security, 2005, Orlando, Florida, United States

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.

Citation Download Citation

Heesung Kwon and 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); https://doi.org/10.1117/12.601834

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