
Proceedings Paper
Target detection in inhomogeneous non-Gaussian hyperspectral data based on nonparametric density estimationFormat | Member Price | Non-Member Price |
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Paper Abstract
Performance of algorithms for target signal detection in Hyperspectral Imagery (HSI) is often deteriorated when the data
is neither statistically homogeneous nor Gaussian or when its Joint Probability Density (JPD) does not match any
presumed particular parametric model. In this paper we propose a novel detection algorithm which first attempts at
dividing data domain into mostly Gaussian and mostly Non-Gaussian (NG) subspaces, and then estimates the JPD of the
NG subspace with a non-parametric Graph-based estimator. It then combines commonly used detection algorithms
operating on the mostly-Gaussian sub-space and an LRT calculated directly with the estimated JPD of the NG sub-space,
to detect anomalies and known additive-type target signals. The algorithm performance is compared to commonly used
algorithms and is found to be superior in some important cases.
Paper Details
Date Published: 29 May 2013
PDF: 11 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87431A (29 May 2013); doi: 10.1117/12.2016452
Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)
PDF: 11 pages
Proc. SPIE 8743, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, 87431A (29 May 2013); doi: 10.1117/12.2016452
Show Author Affiliations
G. A. Tidhar, Ben-Gurion Univ. of the Negev (Israel)
S. R. Rotman, Ben-Gurion Univ. of the Negev (Israel)
Published in SPIE Proceedings Vol. 8743:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX
Sylvia S. Shen; Paul E. Lewis, Editor(s)
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