Hyperspectral Super-Resolution: A Coupled Nonnegative Block-Term Tensor Decomposition Approach

Zhang, Guoyong; Fu, Xiao; Huang, Kejun; Wang, Jun

doi:10.1109/camsap45676.2019.9022476

Cited by 26 publications

(34 citation statements)

References 21 publications

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“…The decomposition of 3-way tensors in rank-(L, L, 1) terms finds many applications in psychometrics, chemometrics, neuroscience, and signal processing, similar to its CPD counterpart. Rank-(L, L, 1) BTD is essentially unique under some mild conditions and its factors have explicit physical interpretations, which has been proven useful for blind source separation [68], [69] in array signal processing [73], spectrum cartography [74], and hyperspectral super-resolution (HSR) [75]. Formally, the rank-(L, L, 1) BTD of a tensor X ∈ R I×J×K is a decomposition of the form…”

Section: The Rank Of a Tensormentioning

confidence: 99%

Tensor Decompositions in Wireless Communications and MIMO Radar

Chen

Ahmad

Vorobyov

et al. 2021

IEEE J. Sel. Top. Signal Process.

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The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods. Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

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Section: The Rank Of a Tensormentioning

confidence: 99%

Tensor Decompositions in Wireless Communications and MIMO Radar

Chen

Ahmad

Vorobyov

et al. 2021

IEEE J. Sel. Top. Signal Process.

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“…In recent years, there have been several attempts which fuse a high resolution multispectral image (HRMSI) with a low resolution hyperspectral image (LRHSI) [1][2][3][4][5][6][7][8][9][10][11][12][13] to produce a high spatio-spectral resolution HSI. Basically, all fusion approaches can be grouped in the following categories: methods using a Bayesian framework [6,11,[14][15][16][17][18], matrix factorization based methods [4,5,12,[19][20][21], tensor factorization based methods [1,3,9,[22][23][24][25] and deep learning based methods [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition

et al. 2021

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Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-spectral structure information when stacking the 3D data structure of a hyperspectral image into a matrix form. Moreover, the spectral, spatial, or their joint structural features have to be imposed from the outside as a constraint to well pose the matrix factorization problem. The proposed method has the advantage of preserving the spatio-spectral structure of hyperspectral images. In this paper, the NTD is directly imposed on the coupled tensors of the HSI and MSI. Hence, the intrinsic spatio-spectral structure of the HSI is represented without loss, and spatial and spectral information can be interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled with the core tensor of the Tucker tensor decomposition. The proposed method is straightforward and easy to implement. Unlike other state-of-the-art approaches, the complexity of the proposed approach is linear with the size of the HSI cube. Experiments on two well-known datasets give promising results when compared with some recent methods from the literature.

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“…Furthermore, the block term decomposition with rank-(L, L, 1) (BTD) model has become an effective approach for HSI processing by integrating the advantages of Tucker and CP decompositions [39], [40]. BTD factorizes third-order HSI data into R component tensors, where each component tensor is approximated by the outer product of a rank-L matrix A r B T r and a column vector c r .…”

Section: Introductionmentioning

confidence: 99%

Nonlocal Block-Term Decomposition for Hyperspectral Image Mixed Noise Removal

Zeng

Huang

Chen

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Since the facility restrictions and weather conditions, hyperspectral image (HSI) is generally seriously polluted by a variety of noises. Recently, the method based on block term decomposition with rank-(L, L, 1) (BTD) has attracted wide attention in HSI mixed noise removal. BTD factorizes thirdorder HSI data into the sum of a series of component tensors, where each of the component tensors is represented by the outer product of a rank-L matrix ArB T r and a column vector cr. BTD has clear physical interpretation because its latent factors ArB T r and cr can be interpreted abundance map and spectral signature respectively. The essential uniqueness of BTD is under the lowrank assumption of ArB T r . However, the low-rank assumption is not always held in real scenarios. The BTD-based method usually sets L to full rank to achieve satisfactory results. In this paper, we suggest a novel model based on nonlocal block term decomposition (NLBTD) for HSI mixed noise removal. More specifically, for each grouped similar image block, BTD is introduced to capture nonlocal self-similarity and global spectral lowrankness, the unidirectional total variation (TV) is introduced to preserve local spectral smoothness. By faithfully exploring nonlocal self-similarity, global spectral low-rankness, and local spectral smoothness, the proposed method is expected to produce promising results with guarantee the essential uniqueness of BTD. To tackle the resulting model, we design an efficient algorithm based on the proximal alternating minimization with the theoretical guarantees. Extensive numerical experiments in HSI mixed noise removal demonstrate that the proposed NLBTD method achieves satisfactory performance compared with stateof-the-art methods.

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Hyperspectral Super-Resolution: A Coupled Nonnegative Block-Term Tensor Decomposition Approach

Cited by 26 publications

References 21 publications

Tensor Decompositions in Wireless Communications and MIMO Radar

Tensor Decompositions in Wireless Communications and MIMO Radar

Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition

Nonlocal Block-Term Decomposition for Hyperspectral Image Mixed Noise Removal

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