Subspace Clustering Constrained Sparse NMF for Hyperspectral Unmixing

Lu, Xiaoqiang; Dong, Le; Yuan, Yuan

doi:10.1109/tgrs.2019.2946751

Cited by 49 publications

(23 citation statements)

References 57 publications

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“…Figure 6 introduces the endmember spectrum comparison curves extracted by six algorithms. Since the Indiana Dataset does not have the ground truth of abundances, here we only show the comparison of the abundance maps in Figure 7, and the results are similar to [8], which is sufficient to prove that the proposed SCLT algorithm has full superiority.…”

Section: Sulrsr-tv Sgsnmf Mv-ntf Nl-tsun Ultra-v Scltmentioning

confidence: 94%

“…This paper uses two commonly used unmixing evaluation indicators [8], including spectral angle distance (SAD) and root-mean-square error (RMSE). Specifically, the known ground truth endmember matrixM and the abundance tensorÂ, SAD and RMSE are calculated as:…”

Section: Evaluation Indexesmentioning

confidence: 99%

“…In the current research, there are two main kinds of unmixing models. One is the linear mixed model (LMM) assuming that the pixels are linearly expressed by all endmembers in the image, and the other is the nonlinear mixed model, which assumes that there are multiple complex interactions among endmembers [8,9]. Since LMM is simple and easy to model, this paper only discusses the unmixing algorithm under the linear mixing assumption.…”

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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Section: Sulrsr-tv Sgsnmf Mv-ntf Nl-tsun Ultra-v Scltmentioning

confidence: 94%

Section: Evaluation Indexesmentioning

confidence: 99%

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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“…In this paper, the spatial information from adjacent pixels refers to the spatial structure information and spectrums contained in adjacent pixels. In practice, due to that adjacent pixels in real MSIs or HSIs potentially corresponding to the same object, adjacent pixels may have similar spectral signatures [50][51][52], which can be used as a prior to refine the reconstruct HR HSI. The adjacent pixels with similar spectral signatures are called homogeneous adjacent pixels.…”

Section: Residual Blockmentioning

confidence: 99%

Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

Chen

Zheng

2021

Remote Sensing

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Recently, many convolutional networks have been built to fuse a low spatial resolution (LR) hyperspectral image (HSI) and a high spatial resolution (HR) multispectral image (MSI) to obtain HR HSIs. However, most deep learning-based methods are supervised methods, which require sufficient HR HSIs for supervised training. Collecting plenty of HR HSIs is laborious and time-consuming. In this paper, a self-supervised spectral-spatial residual network (SSRN) is proposed to alleviate dependence on a mass of HR HSIs. In SSRN, the fusion of HR MSIs and LR HSIs is considered a pixel-wise spectral mapping problem. Firstly, this paper assumes that the spectral mapping between HR MSIs and HR HSIs can be approximated by the spectral mapping between LR MSIs (derived from HR MSIs) and LR HSIs. Secondly, the spectral mapping between LR MSIs and LR HSIs is explored by SSRN. Finally, a self-supervised fine-tuning strategy is proposed to transfer the learned spectral mapping to generate HR HSIs. SSRN does not require HR HSIs as the supervised information in training. Simulated and real hyperspectral databases are utilized to verify the performance of SSRN.

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“…Many of these approaches use a single spectrum to represent one kind of material and only extract a single endmember spectrum for each endmember class. Techniques in this category either directly extract endmembers from the image, for example, the pixel purity index (PPI) [7], N-FINDR [8], and vertex component analysis (VCA) [9], among many others [10]- [12], or they generate virtual endmembers without assuming the presence of pure signatures in the input data, such as minimum volume-based methods [13]- [15] and nonnegative matrix factorization-based methods [16]- [18]. The major drawback of these methods is that they ignore the endmember variability problem within each endmember class.…”

Section: Introductionmentioning

confidence: 99%

Endmember Bundle Extraction Based on Multiobjective Optimization

Liu

Zhu

2021

IEEE Trans. Geosci. Remote Sensing

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A number of endmember extraction methods have been developed to identify pure pixels in hyperspectral images (HSIs). The majority of them use only one spectrum to represent one kind of material, which ignores the spectral variability problem that particularly characterizes a HSI with high spatial resolution. Only a few algorithms have been developed to identify multiple endmembers representing the spectral variability within each class, called endmember bundle extraction (EBE). This article introduces multiobjective particle swarm optimization for the identification of multiple endmember spectra with variability. Unlike existing convex geometry-based EBE methods, which operate on a single geometry of the dataspace, the proposed method divides the observed data into subsets along the spectral dimension and simultaneously operates on multiple dataspaces to obtain candidate endmembers based on multiobjective particle swarm optimization. The candidate endmembers are then refined by spatial post-processing and sequential forward floating selection to produce the final result. Experiments are conducted on both synthetic and real hyperspectral data to demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art methods.

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Subspace Clustering Constrained Sparse NMF for Hyperspectral Unmixing

Cited by 49 publications

References 57 publications

Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing

Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing

Hyperspectral Image Super-Resolution with Self-Supervised Spectral-Spatial Residual Network

Endmember Bundle Extraction Based on Multiobjective Optimization

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