FastKRX: A Fast Approximation for Kernel RX Anomaly Detection

Abstract

In this paper, a fast approximate version of the Kernel RX-algorithm, termed FastKRX is presented. The original Kernel RX-algorithm is reformulated using a spatial weighting function. In the proposed framework, a single kernel Gram matrix is defined over the entire image domain, and the detector statistics for the whole image can be obtained directly from the centered kernel Gram matrix. A methodology based on spatial-spectral clusters is presented for the fast computation of the centered kernel Gram matrix using a multivariate Taylor series approximation. Comparative detection performance on representative airborne multispectral data for both the proposed FastKRX algorithm and the RX anomaly detector is presented. Comparative computational complexity and results on speed of execution are also presented.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Anomaly Detection; Kernel RX; Automatic Target Detection; Gauss Reduction; Mine Detection; Multivariate Taylor Series Approximation

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2008 SPIE, All rights reserved.

Publication Date

01 Apr 2008

Link to Full Text

Share

 
COinS