Kronecker compressive sensing-based mechanism with fully independent sampling dimensions for hyperspectral imaging

Rongqiang Zhao; Qiang Wang; Yi Shen

doi:10.1117/1.JEI.24.6.063012

30 November 2015 Kronecker compressive sensing-based mechanism with fully independent sampling dimensions for hyperspectral imaging

Rongqiang Zhao, Qiang Wang, Yi Shen

Author Affiliations +

Journal of Electronic Imaging, Vol. 24, Issue 6, 063012 (November 2015). https://doi.org/10.1117/1.JEI.24.6.063012

Abstract

We propose a new approach for Kronecker compressive sensing of hyperspectral (HS) images, including the imaging mechanism and the corresponding reconstruction method. The proposed mechanism is able to compress the data of all dimensions when sampling, which can be achieved by three fully independent sampling devices. As a result, the mechanism greatly reduces the control points and memory requirement. In addition, we can also select the suitable sparsifying bases and generate the corresponding optimized sensing matrices or change the distribution of sampling ratio for each dimension independently according to different HS images. As the cooperation of the mechanism, we combine the sparsity model and low multilinear-rank model to develop a reconstruction method. Analysis shows that our reconstruction method has a lower computational complexity than the traditional methods based on sparsity model. Simulations verify that the HS images can be reconstructed successfully with very few measurements. In summary, the proposed approach can reduce the complexity and improve the practicability for HS image compressive sensing.

Citation Download Citation

Rongqiang Zhao, Qiang Wang, and Yi Shen "Kronecker compressive sensing-based mechanism with fully independent sampling dimensions for hyperspectral imaging," Journal of Electronic Imaging 24(6), 063012 (30 November 2015). https://doi.org/10.1117/1.JEI.24.6.063012

Published: 30 November 2015

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