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Proceedings Paper

Random projection-based dimensionality reduction method for hyperspectral target detection
Author(s): Weiyi Feng; Qian Chen; Weiji He; Gonzalo R. Arce; Guohua Gu; Jiayan Zhuang
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Paper Abstract

Dimensionality reduction is a frequent preprocessing step in hyperspectral image analysis. High-dimensional data will cause the issue of the “curse of dimensionality” in the applications of hyperspectral imagery. In this paper, a dimensionality reduction method of hyperspectral images based on random projection (RP) for target detection was investigated. In the application areas of hyperspectral imagery, e.g. target detection, the high dimensionality of the hyperspectral data would lead to burdensome computations. Random projection is attractive in this area because it is data independent and computationally more efficient than other widely-used hyperspectral dimensionality-reduction methods, such as Principal Component Analysis (PCA) or the maximum-noise-fraction (MNF) transform. In RP, the original highdimensional data is projected onto a low dimensional subspace using a random matrix, which is very simple. Theoretical and experimental results indicated that random projections preserved the structure of the original high-dimensional data quite well without introducing significant distortion. In the experiments, Constrained Energy Minimization (CEM) was adopted as the target detector and a RP-based CEM method for hyperspectral target detection was implemented to reveal that random projections might be a good alternative as a dimensionality reduction tool of hyperspectral images to yield improved target detection with higher detection accuracy and lower computation time than other methods.

Paper Details

Date Published: 1 September 2015
PDF: 7 pages
Proc. SPIE 9611, Imaging Spectrometry XX, 961117 (1 September 2015); doi: 10.1117/12.2187709
Show Author Affiliations
Weiyi Feng, Nanjing Univ. of Science and Technology (China)
Univ. of Delaware (United States)
Qian Chen, Nanjing Univ. of Science and Technology (China)
Weiji He, Nanjing Univ. of Science and Technology (China)
Gonzalo R. Arce, Univ. of Delaware (United States)
Guohua Gu, Nanjing Univ. of Science and Technology (China)
Jiayan Zhuang, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9611:
Imaging Spectrometry XX
Thomas S. Pagano; John F. Silny, Editor(s)

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