Spectral–Spatial Robust Nonnegative Matrix Factorization for Hyperspectral Unmixing

Huang, Risheng; Li, Xiaorun; Zhao, Liaoying

doi:10.1109/tgrs.2019.2919166

Cited by 34 publications

(25 citation statements)

References 36 publications

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“…Imbiriba et al [35] propose a low-rank tensor regularization to deal with spectral variability. Non-local low-rank tensor regularizations with weight constraints also show their promoting effects on unmixing in [36,37]. Unfortunately, regularization based on non-local low rank is simulated by the kernel norm and weighted kernel norm in these existing methods.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing

Dong

Yuan

2021

Remote Sensing

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“…Nonnegative matrix factorization (NMF) is a widely used linear HU method [9][10][11][12][13][14][15][16][17][18][19][20]. In this framework, HU is regarded as a blind source separable problem, and decomposes an observed HSI matrix into the product of the pure pixel matrix (endmember matrix) and corresponding proportion matrix (abundance matrix).…”

Section: Introductionmentioning

confidence: 99%

“…As the objective function of NMF is the least squares loss, NMF is sensitive to noise and corresponding unmixing results are usually inaccurate and unstable. To suppress the effect of noise and improve the robustness of the model, many robust NMF methods were proposed [17][18][19][20]. He et al proposed a sparsity-regularized robust NMF by adding a sparse matrix into the linear mixture model to model the sparse noise [17].…”

Section: Introductionmentioning

confidence: 99%

Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing

et al. 2021

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Hyperspectral unmixing (HU) is a research hotspot of hyperspectral remote sensing technology. As a classical HU method, the nonnegative matrix factorization (NMF) unmixing method can decompose an observed hyperspectral data matrix into the product of two nonnegative matrices, i.e., endmember and abundance matrices. Because the objective function of NMF is the traditional least-squares function, NMF is sensitive to noise. In order to improve the robustness of NMF, this paper proposes a maximum likelihood estimation (MLE) based NMF model (MLENMF) for unmixing of hyperspectral images (HSIs), which substitutes the least-squares objective function in traditional NMF by a robust MLE-based loss function. Experimental results on a simulated and two widely used real hyperspectral data sets demonstrate the superiority of our MLENMF over existing NMF methods.

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“…The L 2,1 norm is commonly integrated into sparse NMF to achieve robustness for pixel noise and outlier rejection since it is rotationally invariant [19], [29], [30]. Additionally, the L 1,2 norm is also effective for solving band noise problems [31], [32].…”

Section: Introductionmentioning

confidence: 99%

Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing

Zhou

Zhang

Wang

et al. 2020

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

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Hyperspectral unmixing is a crucial task for hyperspectral images (HSI) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF) and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this paper, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method.

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Spectral–Spatial Robust Nonnegative Matrix Factorization for Hyperspectral Unmixing

Cited by 34 publications

References 36 publications

Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing

Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing

Maximum Likelihood Estimation Based Nonnegative Matrix Factorization for Hyperspectral Unmixing

Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing

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