Improved orthogonal subspace projection algorithm

Jianhui Lv; Yan Piao

doi:10.1117/12.2054291

21 February 2014 Improved orthogonal subspace projection algorithm

Jianhui Lv, Yan Piao

Proceedings Volume 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 (2014) https://doi.org/10.1117/12.2054291
Event: Selected Proceedings of the Photoelectronic Technology Committee Conferences held July-December 2013, 2013, SuZhou, China

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.

Citation Download Citation

Jianhui Lv and Yan Piao "Improved orthogonal subspace projection algorithm", 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); https://doi.org/10.1117/12.2054291

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KEYWORDS

Target detection

Detection and tracking algorithms

Hyperspectral target detection

Signal to noise ratio

Hyperspectral imaging

Filtering (signal processing)

Linear filtering

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