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Proceedings Paper

FastKRX: a fast approximation for kernel RX anomaly detection
Author(s): Spandan Tiwari; Sanjeev Agarwal; Anh Trang
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Paper 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.

Paper Details

Date Published: 29 April 2008
PDF: 12 pages
Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 695316 (29 April 2008); doi: 10.1117/12.779586
Show Author Affiliations
Spandan Tiwari, Migma Systems Inc. (United States)
Sanjeev Agarwal, Missouri Univ. of Science and Technology (United States)
Anh Trang, U.S. Army RDECOM CERDEC NVESD (United States)

Published in SPIE Proceedings Vol. 6953:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
Russell S. Harmon; John H. Holloway Jr.; J. Thomas Broach, Editor(s)

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