Constrained Nonnegative Tensor Factorization for Spectral Unmixing of Hyperspectral Images: A Case Study of Urban Impervious Surface Extraction

Feng, Boyu; Wang, Jinfei

doi:10.1109/lgrs.2018.2877734

Cited by 18 publications

(17 citation statements)

References 9 publications

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“…From the visual comparison, it can be evidently observed that the abundance maps estimated by NMF have more exquisite and detailed edges, while MV-NTF yields in smoother abundance maps. This phenomenon is also consistent with the comparison between MV-NTFbased and NTF-based methods in [38], similarly revealing that the tensor works better with smoother images, whereas has no advantage for high-spatial resolution images containing abundant small-scale details.…”

Section: A Motivationsupporting

confidence: 86%

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Sparsity-Constrained Coupled Nonnegative Matrix–Tensor Factorization for Hyperspectral Unmixing

Liu

Feng

et al. 2020

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

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Hyperspectral unmixing refers to a source separation problem of decomposing a hyperspectral imagery (HSI) to estimate endmembers and their corresponding abundances. Recently, matrix-vector nonnegative tensor factorization (MV-NTF) was proposed for unmixing to avoid structure information loss, which is caused by the HSI cube unfolding in nonnegative matrix factorization (NMF)-based methods. However, MV-NTF ignores local spatial information due to directly dealing with data as a whole, meanwhile, the forceful rank constraint in low-rank tensor decomposition loses some detailed structures. Unlike MV-NTF works at the original data, the pixel-based NMF is more adaptive to learn local spatial variations. Hence, from the perspective of multi-view, it is significant to utilize the complementary advantages of MV-NTF and NMF to fully preserve the intrinsic structure information and exploit more detailed spatial information. In this paper, we propose a sparsityconstrained coupled nonnegative matrix-tensor factorization (SC-NMTF) model for unmixing, wherein MV-NTF and NMF are subtly coupled by sharing endmembers and abundances. Since the representations for abundances in MV-NTF and NMF are distinct, abundance sharing is achieved indirectly by introducing an auxiliary constraint. Furthermore, the L 1/2 regularizer is adopted to promote the sparsity of abundances. A series of experiments on synthetic and real hyperspectral data demonstrate the effectiveness of the proposed SCNMTF method.

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Section: A Motivationsupporting

confidence: 86%

“…The experimental result in [36] shows that MV-NTF outperforms some state-of-the-art NMFbased unmixing methods in most cases. Similar to NMF, three constraints were integrated into MV-NTF in [38], including sparseness, minimum volume, and robust nonlinearity. Xiong et al [39] incorporated TV regularization on abundance maps.…”

Section: Introductionmentioning

confidence: 99%

Sparsity-Constrained Coupled Nonnegative Matrix–Tensor Factorization for Hyperspectral Unmixing

Liu

Feng

et al. 2020

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

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“…Xiong et al [26] apply the TV regularization to the abundance tensor. Sparse constraint and minimum volume constraint are added to MV-NTF to improve the performance of the algorithm in [34]. Imbiriba et al [35] propose a low-rank tensor regularization to deal with spectral variability.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike [22,[33][34][35], in SCLT a novel kind of low-rank tensor regularization is proposed to learn the low-rank structure in HSI. According to prior knowledge, there are non-local similarities in hyperspectral data.…”

Section: Sclt Modelmentioning

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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“…Xiong et al [17] introduced total variation regularization into MVNTF-based unmixing model, by which the global spectral-spatial information and local spatial information were simultaneously exploited. Using the idea of increasing additional constraints, Feng et al [18] improve the plain MVNTF method by integrating sparseness, volume, and nonlinearity constraints into the cost function. Besides, lowrank constraints for abundance and endmember tensors have also been adopted in NTF-based unmixing methods [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Multiple Clustering Guided Nonnegative Matrix Factorization for Hyperspectral Unmixing

Wang

Qian

Liu

2020

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

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Spectral unmixing is an important technique for quantitatively analyzing hyperspectral remote sensing images. Recently, constrained nonnegative matrix factorization (NMF) has been demonstrated to be a powerful tool for spectral unmixing. However, acquiring the problem-dependent prior knowledge and incorporating it into NMF as effective constraints is a challenging task. In this paper, a multiple clustering guided NMF (MCG-NMF) unmixing approach is proposed under a selfsupervised framework which has been used to effectively learn high-level semantic information from the data with a surrogate task in many applications. Specifically, in order to provide selfsupervised information to guide the NMF-based unmixing model, multiple clustering is integrated into the optimization process of NMF. Moreover, by introducing interaction between each clustering and the unmixing procedure, more accurate proximate endmember signatures and proximate abundance distributions are expected to be acquired and used to impose self-supervised constraints on endmembers and abundances respectively. Consequently, effective prior information about endmember signatures and abundance distributions is captured and simultaneously integrated into NMF as valuable constraints to promote unmixing performance. Experiments are conducted on both synthetic data and real HSIs, and the superior performance of our method is shown by comparing it with several state-of-the-art algorithms.

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Constrained Nonnegative Tensor Factorization for Spectral Unmixing of Hyperspectral Images: A Case Study of Urban Impervious Surface Extraction

Cited by 18 publications

References 9 publications

Sparsity-Constrained Coupled Nonnegative Matrix–Tensor Factorization for Hyperspectral Unmixing

Sparsity-Constrained Coupled Nonnegative Matrix–Tensor Factorization for Hyperspectral Unmixing

Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing

Multiple Clustering Guided Nonnegative Matrix Factorization for Hyperspectral Unmixing

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