- Research Article
- Open Access
A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 029250 (2006)
Several linear and nonlinear detection algorithms that are based on spectral matched (subspace) filters are compared. Nonlinear (kernel) versions of these spectral matched detectors are also given and their performance is compared with linear versions. Several well-known matched detectors such as matched subspace detector, orthogonal subspace detector, spectral matched filter, and adaptive subspace detector are extended to their corresponding kernel versions by using the idea of kernel-based learning theory. In kernel-based detection algorithms the data is assumed to be implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function. The expression for each detection algorithm is then derived in the feature space, which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space. Experimental results based on simulated toy examples and real hyperspectral imagery show that the kernel versions of these detectors outperform the conventional linear detectors.
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Kwon, H., Nasrabadi, N.M. A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery. EURASIP J. Adv. Signal Process. 2007, 029250 (2006). https://doi.org/10.1155/2007/29250
- Kernel Function
- Feature Space
- Matched Filter
- Orthogonal Subspace
- Kernel Version