FastKRX: A Fast Approximation for Kernel RX Anomaly Detection
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.
S. Tiwari et al., "FastKRX: A Fast Approximation for Kernel RX Anomaly Detection," Proceedings of SPIE, SPIE, Apr 2008.
The definitive version is available at https://doi.org/10.1117/12.779586
Electrical and Computer Engineering
Keywords and Phrases
Anomaly Detection; Kernel RX; Automatic Target Detection; Gauss Reduction; Mine Detection; Multivariate Taylor Series Approximation
Article - Conference proceedings
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