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Authors: Qi Wang 1 ; Hong Xu 2 ; Lingling Ma 1 ; Chuanrong Li 1 ; Yongsheng Zhou 1 and Lingli Tang 1

Affiliations: 1 Academy of Opto-Electronics and Chinese Academy of Sciences, China ; 2 High Technology Research and Development Center and Ministry of Science and Technology, China

Keyword(s): Compressive sensing, hyperspectral imaging, sparse representation, dictionary learning, reconstruction algorithm

Related Ontology Subjects/Areas/Topics: Computational Optical Sensing and Imaging ; Optics ; Photonics, Optics and Laser Technology ; Spectroscopy, Imaging and Metrology

Abstract: The existing algorithms to reconstruct hyperspectral compressive sensing images mainly use the sparse property of spatial information and some simple non-adaptive spectral constraint such as the low-rank property. However, these strategies cannot remove the spectral redundancy efficiently and a new method to make full use of the abundant redundancy of spectral information and improve the quality for hyperspectral CS reconstruction is necessary. A new CS sampling and reconstruction model based on spectral sparse representation was proposed in this paper. The spectral sparse dictionary was constructed from training samples to enhance the effect of sparse representation and the total variation constraint of spatial images was also considered to further enhance the precision during the reconstruction. The experiment to reconstruct AVIRIS hyperspectral images of 200 bands show that the hyperspectral image was almost perfectly reconstructed at 25% sampling rate and the spatial and spectral precision was higher than traditional methods which only adopt the spatial sparsity and simple non-adaptive spectral constraint in the same condition. (More)

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Paper citation in several formats:
Wang, Q.; Xu, H.; Ma, L.; Li, C.; Zhou, Y. and Tang, L. (2018). Hyperspectral Compressive Sensing Imaging via Spectral Sparse Constraint. In Proceedings of the 6th International Conference on Photonics, Optics and Laser Technology - PHOTOPTICS; ISBN 978-989-758-286-8; ISSN 2184-4364, SciTePress, pages 273-278. DOI: 10.5220/0006664202730278

@conference{photoptics18,
author={Qi Wang. and Hong Xu. and Lingling Ma. and Chuanrong Li. and Yongsheng Zhou. and Lingli Tang.},
title={Hyperspectral Compressive Sensing Imaging via Spectral Sparse Constraint},
booktitle={Proceedings of the 6th International Conference on Photonics, Optics and Laser Technology - PHOTOPTICS},
year={2018},
pages={273-278},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006664202730278},
isbn={978-989-758-286-8},
issn={2184-4364},
}

TY - CONF

JO - Proceedings of the 6th International Conference on Photonics, Optics and Laser Technology - PHOTOPTICS
TI - Hyperspectral Compressive Sensing Imaging via Spectral Sparse Constraint
SN - 978-989-758-286-8
IS - 2184-4364
AU - Wang, Q.
AU - Xu, H.
AU - Ma, L.
AU - Li, C.
AU - Zhou, Y.
AU - Tang, L.
PY - 2018
SP - 273
EP - 278
DO - 10.5220/0006664202730278
PB - SciTePress

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