Research Article
Fast Holo-Kronecker Compressive Sensing for Hyperspectral Image
@INPROCEEDINGS{10.4108/eai.15-8-2015.2261161, author={Rongqiang Zhao and Qiang Wang and Yi Shen}, title={Fast Holo-Kronecker Compressive Sensing for Hyperspectral Image}, proceedings={10th EAI International Conference on Communications and Networking in China}, publisher={IEEE}, proceedings_a={CHINACOM}, year={2015}, month={9}, keywords={hyperspectral image compressive sensing kronecker product low multilinear-rank}, doi={10.4108/eai.15-8-2015.2261161} }- Rongqiang Zhao
Qiang Wang
Yi Shen
Year: 2015
Fast Holo-Kronecker Compressive Sensing for Hyperspectral Image
CHINACOM
IEEE
DOI: 10.4108/eai.15-8-2015.2261161
Abstract
Compressive sensing of hyperspectral image (HSI) faces the difficulties of complex computation and much information redundancies. In this paper, we propose a highly-efficient compressive sensing framework including sampling method and its corresponding reconstruction algorithm for HSI. Kronecker product is used to generate the sparsifying basis and measurement matrices. Both the data in spatial dimensions and spectral dimension are compressed, resulting an enhanced sampling efficiency. Very few measurements are needed for a successful reconstruction. We combine the sparsity model and low multilinear-rank model for fast and accurate reconstruction. Iterative algorithm is employed to reconstruct the data only in one dimension of HSI independently instead of all dimensions globally, which can speed up the reconstruction.
