Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/102908
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Type: Journal article
Title: Dictionary learning for promoting structured sparsity in hyperspectral compressive sensing
Author: Zhang, L.
Wei, W.
Zhang, Y.
Shen, C.
Van Den Hengel, A.
Shi, Q.
Citation: IEEE Transactions on Geoscience and Remote Sensing, 2016; 54(12):7223-7235
Publisher: IEEE Publisher
Issue Date: 2016
ISSN: 0196-2892
1558-0644
Statement of
Responsibility: 
Lei Zhang, WeiWei, Yanning Zhang, Chunhua Shen, Anton van den Hengel and Qinfeng Shi
Abstract: The ability to accurately represent a hyperspectral image (HSI) as a combination of a small number of elements from an appropriate dictionary underpins much of the recent progress in hyperspectral compressive sensing (HCS). Preserving structure in the sparse representation is critical to achieving an accurate reconstruction but has thus far only been partially exploited because existing methods assume a predefined dictionary. To address this problem, a structured sparsity-based hyperspectral blind compressive sensing method is presented in this study. For the reconstructed HSI, a data-adaptive dictionary is learned directly from its noisy measurements, which promotes the underlying structured sparsity and obviously improves reconstruction accuracy. Specifically, a fully structured dictionary prior is first proposed to jointly depict the structure in each dictionary atom as well as the correlation between atoms, where the magnitude of each atom is also regularized. Then, a reweighted Laplace prior is employed to model the structured sparsity in the representation of the HSI. Based on these two priors, a unified optimization framework is proposed to learn both the dictionary and sparse representation from the measurements by alternatively optimizing two separate latent variable Bayes models.With the learned dictionary, the structured sparsity of HSIs can be well described by the reweighted Laplace prior. In addition, both the learned dictionary and sparse representation are robust to noise corruption in the measurements. Extensive experiments on three hyperspectral data sets demonstrate that the proposed method outperforms several state-of-the-art HCS methods in terms of the reconstruction accuracy achieved.
Keywords: Dictionary learning; hyperspectral compressive sensing (HCS); latent variable Bayes model; structured sparsity
RMID: 0030056046
DOI: 10.1109/TGRS.2016.2598577
Grant ID: http://purl.org/au-research/grants/arc/DP140102270
http://purl.org/au-research/grants/arc/DP160100703
http://purl.org/au-research/grants/arc/FT120100969
Appears in Collections:Computer Science publications

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