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Cited by 821

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Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery

Heesung Kwon, Nasrabadi, N.M.·2005·IEEE Transactions on Geoscience and Remote Sensing
DOI 10.1109/tgrs.2004.841487
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Abstract

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 nonlinear mapping function. However, in this paper it is shown that the kernel RX-algorithm can easily be implemented by kernelizing the RX-algorithm in the feature space in terms of kernels that implicitly compute dot products in the feature space. Improved performance of the kernel RX-algorithm over the conventional RX-algorithm is shown by testing several hyperspectral imagery for military target and mine detection.

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Research works printed but not linked: 28

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Metadata

Authors
Kwon, H., & Nasrabadi, N. M.
Venue
IEEE Transactions on Geoscience and Remote Sensing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2005-02
Volume
43
Issue
2
Pages
388-397
ISSN
0196-2892 (ISSN-L)
DOI
10.1109/tgrs.2004.841487
OpenAlex
W2124463804
Cited by
821
References
1
FWCI
52.73
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery. https://app.cochat.ai/papers/10.1109/tgrs.2004.841487
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Kwon, H., & Nasrabadi, N. M. (2005). Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/tgrs.2004.841487

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