Hyperspectral Unmixing via Total Variation Regularized Nonnegative Tensor Factorization

Xiong, Fengchao; Qian, Yuntao; Zhou, Jun; Tang, Yuan Yan

doi:10.1109/tgrs.2018.2872888

Cited by 87 publications

(70 citation statements)

References 51 publications

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“…In this section, the experiments on synthetic and real dataset are conducted to verify the effectiveness of the proposed method. The state-of-the-arts method including VCA-FCLS [6], 2 [39], MVC-NMF [12], 3 L 1/2 -NMF [13], GLNMF [14], TV-RSNMF [15], 4 MVNTF-TV [25], and DLNMF-TV [10] are utilized as comparison methods. All the experiments are carried out on a Windows 7 system with 3.4-GHz Intel Core i7 CPU and 16-GB RAM using MATLAB2016b.…”

Section: Resultsmentioning

confidence: 99%

“…This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ Total variation (TV) regularization is another popular spatial smoothing model in recent years, because the TV can preserve the image edge information and promote the piecewise smoothness of the abundance maps [15], [25]- [28]. In [15], a HTV (band-by-band TV) was embedded into a weighted L 1 -norm sparse framework, and He et al proposed a TV regularized reweighted sparse NMF.…”

Section: Introductionmentioning

confidence: 99%

“…In [15], a HTV (band-by-band TV) was embedded into a weighted L 1 -norm sparse framework, and He et al proposed a TV regularized reweighted sparse NMF. The TV was also introduced into nonnegative tensor factorization (NTF) [25] and deep multilayer NMF [10].…”

Section: Introductionmentioning

confidence: 99%

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Multiple-Priors Ensemble Constrained Nonnegative Matrix Factorization for Spectral Unmixing

Bao

2020

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

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Nonnegative matrix factorization (NMF) is widely used in unmixing issue in recent years, because it can simultaneously estimate the endmembers and abundances. However, most existing NMF-based methods only consider single matrix constraints and the other one is ignored. In fact, due to the influence of various noise, the regularization effectiveness based on the single matrix constraint method may be limited. In addition, hyperspectral images contain a variety of prior information, while many approaches usually only consider one of the priors, and the synergistic effect of multiple priors unions and two matrix joint constraints is neglected. In this article, to overcome this limitation, we propose a new blind unmixing scheme, called multiple-priors ensemble constrained NMF. The article first analyses the HSI intrinsic feature priors from both geometric and statistical aspects, and three important priors learners are defined. Then, three learners are jointly introduced into the NMF model and work together for the first time to impose constraints on both the endmember and the abundance matrix. In order to effectively solve the proposed model, Barzilai-Borwein stepsize strategy accelerates optimization algorithm is developed by using the variable splitting and augmented Lagrangian framework. The effectiveness and superiority of the proposed method are demonstrated by comparing with other advanced approaches on both synthetic and real world datasets.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiple-Priors Ensemble Constrained Nonnegative Matrix Factorization for Spectral Unmixing

Bao

2020

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

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“…For example, sparse unmixing via variable splitting augmented Lagrangian and total variation (SUnSAL-TV) [15] involves a two-dimensional TV regularization. Other TV-based variants include TV with 1 [16], TV with sparse NMF [17], TV with nonnegative tensor factorization [18], and an improved collaborative NMF with TV (ICoNMF-TV) [19] that combines robust collaborative NMF (R-CoNMF) [20] and TV. Recently, TV is considered as a quadratic regularization promoting minimum volume in the NMF framework, referred to as NMF-QMV [21].…”

Section: Introductionmentioning

confidence: 99%

Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization

Qin

Lee

Jocelyn

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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Remote sensing data from hyperspectral cameras suffer from limited spatial resolution, in which a single pixel of a hyperspectral image may contain information from several materials in the field of view. Blind hyperspectral image unmixing is the process of identifying the pure spectra of individual materials (i.e., endmembers) and their proportions (i.e., abundances) at each pixel. In this paper, we propose a novel blind hyperspectral unmixing model based on the graph total variation (gTV) regularization, which can be solved efficiently by the alternating direction method of multipliers (ADMM). To further alleviate the computational cost, we apply the Nyström method to approximate a fully-connected graph by a small subset of sampled points. Furthermore, we adopt the Merriman-Bence-Osher (MBO) scheme to solve the gTV-involved subproblem in ADMM by decomposing a grayscale image into a bit-wise form. A variety of numerical experiments on synthetic and real hyperspectral images are conducted, showcasing the potential of the proposed method in terms of identification accuracy and computational efficiency.

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“…Hence, spatial information has been recognized, utilized, and incorporated in the traditional spectral unmixing model as prior knowledge, such as sparse unmixing via variable splitting augmented Lagrangian and total variation (SUnSAL-TV) [38], non-local sparse unmixing (NLSU) [39], and collaborative SUnSAL [40,41]. In these models, spatial correlations has been developed from a local pixel between a pixel and pixels [46][47][48] to a non-local block [49,50] among searching windows, and different spatial information is considered in form of different regularizations, such as neighborhood filters [51], variational forms [52][53][54]. Different methods have their unique performance.…”

Section: Introductionmentioning

confidence: 99%

Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

2019

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Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression. In a spatial regularized sparse unmixing model, spatial consideration acts as an important role and develops from local neighborhood pixels to global structures. However, incorporating spatial relationships will increase the computational complexity, and it is inevitable that some negative influences obtained by inaccurate estimated abundances’ spatial correlations will reduce the accuracy of the algorithms. To obtain a more reliable and efficient spatial regularized sparse unmixing results, a joint local block grouping with noise-adjusted principal component analysis for hyperspectral remote-sensing imagery sparse unmixing is proposed in this paper. In this work, local block grouping is first utilized to gather and classify abundant spatial information in local blocks, and noise-adjusted principal component analysis is used to compress these series of classified local blocks and select the most significant ones. Then the representative spatial correlations are drawn and replace the traditional spatial regularization in the spatial regularized sparse unmixing method. Compared with total variation-based and non-local means-based sparse unmixing algorithms, the proposed approach can yield comparable experimental results with three simulated hyperspectral data cubes and two real hyperspectral remote-sensing images.

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Hyperspectral Unmixing via Total Variation Regularized Nonnegative Tensor Factorization

Cited by 87 publications

References 51 publications

Multiple-Priors Ensemble Constrained Nonnegative Matrix Factorization for Spectral Unmixing

Multiple-Priors Ensemble Constrained Nonnegative Matrix Factorization for Spectral Unmixing

Blind Hyperspectral Unmixing Based on Graph Total Variation Regularization

Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing

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