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Kernel Oblique Subspace Projection Approach for Target Detection in Hyperspectral Imagery.
- Source :
- Artificial Intelligence & Computational Intelligence (9783642165290); 2010, p422-431, 10p
- Publication Year :
- 2010
-
Abstract
- In this paper, a kernel-based nonlinear version of the oblique subspace projection (OBSP) operator is defined in terms of kernel functions. Input data are implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function. The OBSP expression 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. The resulting kernelized OBSP algorithm is equivalent to a nonlinear OBSP in the original input space. Experimental results based on simulated hyperspectral data and real hyperspectral imagery shows that the kernel oblique subspace projection (KOBSP) outperforms the conventional OBSP. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783642165290
- Database :
- Complementary Index
- Journal :
- Artificial Intelligence & Computational Intelligence (9783642165290)
- Publication Type :
- Book
- Accession number :
- 76774598
- Full Text :
- https://doi.org/10.1007/978-3-642-16530-6_50