Minimum distance constrained non-negative matrix factorization for the endmember extraction of hyperspectral images

Yu, Yue; Guo, Shan; Sun, Weidong

doi:10.1117/12.748379

Cited by 34 publications

(31 citation statements)

References 10 publications

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“…However, if endmembers are unknown or wrongly extracted (see Figure 6b), we should further estimate the endmembers. Fortunately, with the use of the obtained linearly-mixed projections of pixels, the minimum-volume-constrained NMF [8][9][10][11][12] can be further adopted to update the endmembers and abundances simultaneously for highly-mixed data. The newly-updated endmembers can be applied to project pixels to their new linear mixture components (i.e., Equation (11)), alternately.…”

Section: Nonlinear Hyperplanes and Geometric Projectionmentioning

confidence: 99%

“…To deal with this problem, minimum-volume-based methods have been proposed for unmixing [8][9][10][11][12]. In [11,12], nonnegative matrix factorization (NMF) [13] was combined with simplex volume regularizers to estimate the endmembers and abundances simultaneously for highly-mixed data.…”

Section: Introductionmentioning

confidence: 99%

“…In [11,12], nonnegative matrix factorization (NMF) [13] was combined with simplex volume regularizers to estimate the endmembers and abundances simultaneously for highly-mixed data. NMF is usually adopted for solving the blind source separation (BSS) problem, but the cost function's non-convexity impedes its direct use in unsupervised unmixing.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Nonlinear Hyperspectral Unmixing Based on Bilinear Mixture Models via Geometric Projection and Constrained Nonnegative Matrix Factorization

Yang

Wang

2018

Remote Sensing

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Bilinear mixture model-based methods have recently shown promising capability in nonlinear spectral unmixing. However, relying on the endmembers extracted in advance, their unmixing accuracies decrease, especially when the data is highly mixed. In this paper, a strategy of geometric projection has been provided and combined with constrained nonnegative matrix factorization for unsupervised nonlinear spectral unmixing. According to the characteristics of bilinear mixture models, a set of facets are determined, each of which represents the partial nonlinearity neglecting one endmember. Then, pixels' barycentric coordinates with respect to every endmember are calculated in several newly constructed simplices using a distance measure. In this way, pixels can be projected into their approximate linear mixture components, which reduces greatly the impact of collinearity. Different from relevant nonlinear unmixing methods in the literature, this procedure effectively facilitates a more accurate estimation of endmembers and abundances in constrained nonnegative matrix factorization. The updated endmembers are further used to reconstruct the facets and get pixels' new projections. Finally, endmembers, abundances, and pixels' projections are updated alternately until a satisfactory result is obtained. The superior performance of the proposed algorithm in nonlinear spectral unmixing has been validated through experiments with both synthetic and real hyperspectral data, where traditional and state-of-the-art algorithms are compared.

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Section: Nonlinear Hyperplanes and Geometric Projectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unsupervised Nonlinear Hyperspectral Unmixing Based on Bilinear Mixture Models via Geometric Projection and Constrained Nonnegative Matrix Factorization

Yang

Wang

2018

Remote Sensing

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“…The final F-NMF update rules to minimize f with all these considerations are derived from (6), (9), (11), (13), (15), and (17). Thus…”

Section: Minimum Distance Constraintmentioning

confidence: 99%

“…In [14], a minimum volume constrained NMF (MVC-NMF) based on projected gradient (PG) optimization method is proposed, whose regularization term minimizes the simplex volume spanned by the endmembers. Other authors [15] propose a minimum distance constrained NMF (MDC-NMF), which consider the endmember distance instead of the volume of the estimated simplex. MDC-NMF makes a slight modification of the optimized algorithm used for MVC-NMF.…”

Section: Introductionmentioning

confidence: 99%

HALS-based NMF with flexible constraints for hyperspectral unmixing

Chen

Guillaume

2012

EURASIP J. Adv. Signal Process.

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In this article, the hyperspectral unmixing problem is solved with the nonnegative matrix factorization (NMF) algorithm. The regularized criterion is minimized with a hierarchical alternating least squares (HALS) scheme. Under the HALS framework, four constraints are introduced to improve the unmixing accuracy, including the sum-to-unity constraint, the constraints for minimum spectral dispersion and maximum spatial dispersion, and the minimum volume constraint. The derived algorithm is called F-NMF, for NMF with flexible constraints. We experimentally compare F-NMF with different constraints and combined ones. We test the sensitivity and robustness of F-NMF to many parameters such as the purity level of endmembers, the number of endmembers and pixels, the SNR, the sparsity level of abundances, and the overestimation of endmembers. The proposed algorithm improves the results estimated by vertex component analysis. A comparative analysis on real data is included. The unmixing results given by a geometrical method, the simplex identification via split augmented Lagrangian and the F-NMF algorithms with combined constraints are compared, which shows the relative stability of F-NMF.

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Hyperspectral Image Unmixing Based on Sparse and Minimum Volume Constrained Nonnegative Matrix Factorization

Denggang

2014

Communications in Computer and Information Science

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Minimum distance constrained non-negative matrix factorization for the endmember extraction of hyperspectral images

Cited by 34 publications

References 10 publications

Unsupervised Nonlinear Hyperspectral Unmixing Based on Bilinear Mixture Models via Geometric Projection and Constrained Nonnegative Matrix Factorization

Unsupervised Nonlinear Hyperspectral Unmixing Based on Bilinear Mixture Models via Geometric Projection and Constrained Nonnegative Matrix Factorization

HALS-based NMF with flexible constraints for hyperspectral unmixing

Hyperspectral Image Unmixing Based on Sparse and Minimum Volume Constrained Nonnegative Matrix Factorization

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