FastKRX: a fast approximation for kernel RX anomaly detection

Spandan Tiwari; Sanjeev Agarwal; Anh Trang

doi:10.1117/12.779586

29 April 2008 FastKRX: a fast approximation for kernel RX anomaly detection

Spandan Tiwari, Sanjeev Agarwal, Anh Trang

Proceedings Volume 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII; 695316 (2008) https://doi.org/10.1117/12.779586
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

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.

Citation Download Citation

Spandan Tiwari, Sanjeev Agarwal, and Anh Trang "FastKRX: a fast approximation for kernel RX anomaly detection", Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 695316 (29 April 2008); https://doi.org/10.1117/12.779586

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