Abstract
In this paper, we compare several detection algorithms that are based on spectral matched (subspace) filters. Nonlinear (kernel) versions of these spectral matched (subspace) detectors are also discussed and their performance is compared with the linear versions. Several well-known matched detectors, such as matched subspace detector, orthogonal subspace detector, spectral matched filter and adaptive subspace detector (adaptive cosine estimator) are extended to their corresponding kernel versions by using the idea of kernel-based learning theory. In kernel-based detection algorithms the data is implicitly mapped into a high dimensional kernel feature space by a nonlinear mapping which is associated with a kernel function. The 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 real hyperspectral imagery show that the kernel versions of these detectors outperform the conventional linear detectors.
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Keywords
- Feature Space
- High Dimensional Feature Space
- Constant False Alarm Rate
- Kernel Trick
- Generalize Likelihood Ratio Test
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© 2007 Springer-Verlag Berlin Heidelberg
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Nasrabadi, N.M. (2007). Kernel-Based Spectral Matched Signal Detectors for Hyperspectral Target Detection. In: Ghosh, A., De, R.K., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2007. Lecture Notes in Computer Science, vol 4815. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77046-6_9
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DOI: https://doi.org/10.1007/978-3-540-77046-6_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77045-9
Online ISBN: 978-3-540-77046-6
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