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Proceedings Paper

Improved orthogonal subspace projection algorithm
Author(s): Jianhui Lv; Yan Piao
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Paper Abstract

For traditional orthogonal subspace projection method, before performing hyperspectral image target detection, we must acquire the background spectrum vectors. However, in many cases, we cannot obtain the prior knowledge of the background spectrum accurately. And constrained energy minimization algorithm detect targets without a priori information of background spectrum, but the algorithm has a poor performance on the big target detection and cannot effectively extract the target contour. For this reason, we propose a sample weighted orthogonal subspace projection algorithm by defining the weighted autocorrelation matrix to estimation background, and then use the orthogonal subspace projection method to detect the targets. The algorithm effectively reduces the proportion of target pixels in the sample autocorrelation matrix, and has better inhibitory effect to the background. It overcomes the inherent defects of orthogonal subspace projection and constrained energy minimization, the experimental results shows better detection effect.

Paper Details

Date Published: 21 February 2014
PDF: 6 pages
Proc. SPIE 9142, Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013, 91420Q (21 February 2014); doi: 10.1117/12.2054291
Show Author Affiliations
Jianhui Lv, Changchun Univ. of Science and Technology (China)
Yan Piao, Changchun Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 9142:
Selected Papers from Conferences of the Photoelectronic Technology Committee of the Chinese Society of Astronautics: Optical Imaging, Remote Sensing, and Laser-Matter Interaction 2013
Jorge Ojeda-Castaneda; Shensheng Han; Ping Jia; Jiancheng Fang; Dianyuan Fan; Liejia Qian; Yuqiu Gu; Xueqing Yan, Editor(s)

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