Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior

Zheng, Zihao; Wu, Kan; Shim,; Sun, Dong

doi:10.3390/rs11242897

Cited by 13 publications

(6 citation statements)

References 68 publications

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“…The objective function in [32] is similar to (20), hence it can be known that the update rule of Q 2 is:…”

Section: Optimizationmentioning

confidence: 99%

“…However, the NMF framework that regards unmixing as a blind source separation problem is non-convex problem with respect to two matrices obtained, which is greatly affected by the initial value and the solution is not stable enough [11]. Therefore, to improve the performance of unmixing, many regularizations are added to the NMF framework based on the prior knowledge about hyperspectral data, such as sparse regularization [12][13][14], smoothing regularization [15,16], non-local similarity regularization [17], manifold regularization [18], and low-rank regularization [19,20]. Hong et al [21] embed the spectral variability dictionary learning into the linear NMF framework to make the endmembers more accurate.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing

Dong

Yuan

2021

Remote Sensing

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Recently, non-negative tensor factorization (NTF) as a very powerful tool has attracted the attention of researchers. It is used in the unmixing of hyperspectral images (HSI) due to its excellent expression ability without any information loss when describing data. However, most of the existing unmixing methods based on NTF fail to fully explore the unique properties of data, for example, low rank, that exists in both the spectral and spatial domains. To explore this low-rank structure, in this paper we learn the different low-rank representations of HSI in the spectral, spatial and non-local similarity modes. Firstly, HSI is divided into many patches, and these patches are clustered multiple groups according to the similarity. Each similarity group can constitute a 4-D tensor, including two spatial modes, a spectral mode and a non-local similarity mode, which has strong low-rank properties. Secondly, a low-rank regularization with logarithmic function is designed and embedded in the NTF framework, which simulates the spatial, spectral and non-local similarity modes of these 4-D tensors. In addition, the sparsity of the abundance tensor is also integrated into the unmixing framework to improve the unmixing performance through the L2,1 norm. Experiments on three real data sets illustrate the stability and effectiveness of our algorithm compared with five state-of-the-art methods.

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“…The objective function in [32] is similar to (20), hence it can be known that the update rule of Q 2 is:…”

Section: Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing

Dong

Yuan

2021

Remote Sensing

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“…Update iteration i = i + 1 12. end while hyperspectral datasets and one real hyperspectral dataset. We compare the unmixing performance of the proposed method with the performance of several state-of-the-art methods, such as the CLSUnSAL method [40], SUnSAL with TV regularization (SUnSAL-TV) method [45], joint local abundance sparse unmixing (J-LASU) method [60], sparse unmixing with l 1 -l 2 sparsity and TV regularization (l 1 -l 2 SUnSAL-TV) method [74], and the sparse unmixing with nonlocal low-rank prior (NLLRSU) method [75].…”

Section: Initialization: Setmentioning

confidence: 99%

Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images

Sun

Zhan

et al. 2020

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

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The low spatial resolution of hyperspectral images leads to the coexistence of multiple ground objects in a single pixel (called mixed pixels). A large number of mixed pixels in a hyperspectral image hinders the subsequent analysis and application of the image. In order to solve this problem, a novel sparse unmixing method, which considers highly similar patches in nonlocal regions of a hyperspectral image, is proposed in this article. This method exploits spectral correlation by using collaborative sparsity regularization and spatial information by employing total variation and weighted nonlocal low-rank tensor regularization. To effectively utilize the tensor decomposition, nonlocal similar patches are first grouped together. Then, these nonlocal patches are stacked to form a patch group tensor. Finally, weighted low-rank tensor regularization is enforced to constrain the patch group to obtain an estimated low-rank abundance image. Experiments on simulated and real hyperspectral datasets validated the superiority of the proposed method in better maintaining fine details and obtaining better unmixing results.

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“…Reference [20] combines the joint-sparse-blocks with low-rank constraint. Reference [21] proposed nonlocal low-rank prior sparse unmixing algorithm and achieved promising results.…”

Section: Introductionmentioning

confidence: 99%

Double Regression‐Based Sparse Unmixing for Hyperspectral Images

Zhang

et al. 2021

Journal of Sensors

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Sparse unmixing has attracted widespread attention from researchers, and many effective unmixing algorithms have been proposed in recent years. However, most algorithms improve the unmixing accuracy at the cost of large calculations. Higher unmixing accuracy often leads to higher computational complexity. To solve this problem, we propose a novel double regression-based sparse unmixing model (DRSUM), which can obtain better unmixing results with lower computational complexity. DRSUM decomposes the complex objective function into two simple formulas and completes the unmixing process through two sparse regressions. The unmixing result of the first sparse regression is added as a constraint to the second. DRSUM is an open model, and we can add different constraints to improve the unmixing accuracy. In addition, we can perform appropriate preprocessing to further improve the unmixing results. Under this model, a specific algorithm called double regression-based sparse unmixing via K -means ( DRSU M K − means ) is proposed. The improved K -means clustering algorithm is first used for preprocessing, and then we impose single sparsity and joint sparsity (using l 2 , 0 norm to control the sparsity) constraints on the first and second sparse unmixing, respectively. To meet the sparsity requirement, we introduce the row-hard-threshold function to solve the l 2 , 0 norm directly. Then, DRSU M K − means can be efficiently solved under alternating direction method of multipliers (ADMM) framework. Simulated and real data experiments have proven the effectiveness of DRSU M K − means .

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Sparse Unmixing for Hyperspectral Image with Nonlocal Low-Rank Prior

Cited by 13 publications

References 68 publications

Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing

Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing

Weighted Nonlocal Low-Rank Tensor Decomposition Method for Sparse Unmixing of Hyperspectral Images

Double Regression‐Based Sparse Unmixing for Hyperspectral Images

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