Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
ACM Computing Surveys (CSUR)
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Hyperspectral Imaging: Techniques for Spectral Detection and Classification
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
The CFAR adaptive subspace detector is a scale-invariant GLRT
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Nonlinear kernel-based statistical pattern analysis
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Kernel-based spectral matched signal detectors for hyperspectral target detection
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
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In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.