
Proceedings Paper
Kernel-based constrained energy minimization (K-CEM)Format | Member Price | Non-Member Price |
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Paper Abstract
Kernel-based approaches have recently drawn considerable interests in hyperspectral image analysis due to its ability in
expanding features to a higher dimensional space via a nonlinear mapping function. Many well-known detection and
classification techniques such as Orthogonal Subspace Projection (OSP), RX algorithm, linear discriminant analysis,
Principal Components Analysis (PCA), Independent Component Analysis (ICA), have been extended to the
corresponding kernel versions. Interestingly, a target detection method, called Constrained Energy Minimization (CEM)
which has been also widely used in hyperspectral target detection has not been extended to its kernel version. This paper
investigates a kernel-based CEM, called Kernel CEM (K-CEM) which employs various kernels to expand the original
data space to a higher dimensional feature space that CEM can be operated on. Experiments are conducted to perform a
comparative analysis and study between CEM and K-CEM. The results do not show K-CEM provided significant
improvement over CEM in detecting hyperspectral targets but does show significant improvement in detecting targets in
multispectral imagery which provides limited spectral information for the CEM to work well.
Paper Details
Date Published: 11 April 2008
PDF: 11 pages
Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69661S (11 April 2008); doi: 10.1117/12.782221
Published in SPIE Proceedings Vol. 6966:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV
Sylvia S. Shen; Paul E. Lewis, Editor(s)
PDF: 11 pages
Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69661S (11 April 2008); doi: 10.1117/12.782221
Show Author Affiliations
Xiaoli Jiao, Univ. of Maryland, Baltimore County (United States)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)
Published in SPIE Proceedings Vol. 6966:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV
Sylvia S. Shen; Paul E. Lewis, Editor(s)
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