Back to Search
Start Over
Kernel RX-Algorithm: A Nonlinear Anomaly Detector for Hyperspectral Imagery.
- Source :
-
IEEE Transactions on Geoscience & Remote Sensing . Feb2005, Vol. 43 Issue 2, p388-397. 10p. - Publication Year :
- 2005
-
Abstract
- In this paper, 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 non- linear 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01962892
- Volume :
- 43
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Geoscience & Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 16068151
- Full Text :
- https://doi.org/10.1109/TGRS.2004.841487