Random projection-based dimensionality reduction method for hyperspectral target detection

Weiyi Feng; Qian Chen; Weiji He; Gonzalo R. Arce; Guohua Gu; Jiayan Zhuang

doi:10.1117/12.2187709

1 September 2015 Random projection-based dimensionality reduction method for hyperspectral target detection

Weiyi Feng, Qian Chen, Weiji He, Gonzalo R. Arce, Guohua Gu, Jiayan Zhuang

Proceedings Volume 9611, Imaging Spectrometry XX; 961117 (2015) https://doi.org/10.1117/12.2187709
Event: SPIE Optical Engineering + Applications, 2015, San Diego, California, United States

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.

Citation Download Citation

Weiyi Feng, Qian Chen, Weiji He, Gonzalo R. Arce, Guohua Gu, and Jiayan Zhuang "Random projection-based dimensionality reduction method for hyperspectral target detection", Proc. SPIE 9611, Imaging Spectrometry XX, 961117 (1 September 2015); https://doi.org/10.1117/12.2187709

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available