Correlation estimation for remote sensing compressed-sensed video sampling

Sheng-liang Li; Kun Liu; Li Zhang; Jie Wang; Zhi-zhou Zhang; Da-peng Han

doi:10.1117/1.JEI.23.6.063007

18 November 2014 Correlation estimation for remote sensing compressed-sensed video sampling

Sheng-liang Li, Kun Liu, Li Zhang, Jie Wang, Zhi-zhou Zhang, Da-peng Han

Author Affiliations +

Journal of Electronic Imaging, Vol. 23, Issue 6, 063007 (November 2014). https://doi.org/10.1117/1.JEI.23.6.063007

Abstract

Compressed sensing (CS) is a new signal processing theory that provides an insight into signal processing. The CS theory has numerous potential applications in various fields, such as image processing, astronomical data analysis, analog-to-information, medical imaging, and remote sensing (RS) imagery. The CS theory is applied to RS video imagery. An RS video based on a compressed sensing (RS-VCS) framework with correlation estimation measurement is proposed, along with a block measurement correlation model and corresponding reconstruction. The linearized Bregman algorithm is used to solve the reconstruction model, and the performance of the RS-VCS framework is simulated numerically.

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Sheng-liang Li, Kun Liu, Li Zhang, Jie Wang, Zhi-zhou Zhang, and Da-peng Han "Correlation estimation for remote sensing compressed-sensed video sampling," Journal of Electronic Imaging 23(6), 063007 (18 November 2014). https://doi.org/10.1117/1.JEI.23.6.063007

Published: 18 November 2014

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KEYWORDS

Video

Remote sensing

Video compression

Reconstruction algorithms

Data modeling

Image restoration

Compressed sensing

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