Hyperspectral Unmixing Based on Incremental Kernel Nonnegative Matrix Factorization

Huang, Risheng; Li, Xiaorun; Zhao, Liaoying

doi:10.1109/tgrs.2018.2841036

Cited by 19 publications

(7 citation statements)

References 24 publications

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“…The statistical algorithms identify the endmembers and their corresponding abundances at the same time by utilizing the statistical properties of the HSI. Popular statistical algorithms include independent component analysis (ICA) [32], [33], nonnegative matrix factorization (NMF) [34], [35], and Bayesian approaches [36]- [38]. Among them, NMF provides a good fit for hyperspectral unmixing owing to its nonnegativity and interpretability.…”

Section: MVmentioning

confidence: 99%

“…For dealing with large-scale and streaming dynamic data, Zhu et al [138] proposed an online KNMF (OKNMF) framework to control the computational complexity via adopting the stochastic gradient descent (SGD), mini-batch, and averaged SGD (ASGD) strategies. In addition, the KNMF was extended to incremental KNMF (IKNMF) and improved IKNMF (IIKNMF) for desired unmixing accuracy and efficiency [34].…”

Section: B Kernelized Nmfmentioning

confidence: 99%

See 1 more Smart Citation

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

Feng

Wang

et al. 2022

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

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Hyperspectral unmixing has been an important technique that estimates a set of endmembers and their corresponding abundances from a hyperspectral image (HSI). Nonnegative matrix factorization (NMF) plays an increasingly significant role in solving this problem. In this article, we present a comprehensive survey of the NMF-based methods proposed for hyperspectral unmixing. Taking the NMF model as a baseline, we show how to improve NMF by utilizing the main properties of HSIs (e.g., spectral, spatial, and structural information). We categorize three important development directions including constrained NMF, structured NMF, and generalized NMF. Furthermore, several experiments are conducted to illustrate the effectiveness of associated algorithms. Finally, we conclude the article with possible future directions with the purposes of providing guidelines and inspiration to promote the development of hyperspectral unmixing.

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Section: MVmentioning

confidence: 99%

Section: B Kernelized Nmfmentioning

confidence: 99%

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

Feng

Wang

et al. 2022

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

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“…Research work has been devoted to HU. Among it, non-negative matrix factorization (NMF) has been shown to be a useful unsupervised decomposition for hyperspectral unmixing [6]. The learned non-negative basis vectors that are used are distributed, yet they are still sparse combinations that generate expressiveness in the signal reconstructions [7].…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing

Zou

Lan

2019

Remote Sensing

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Due to the complex background and low spatial resolution of the hyperspectral sensor, observed ground reflectance is often mixed at the pixel level. Hyperspectral unmixing (HU) is a hot-issue in the remote sensing area because it can decompose the observed mixed pixel reflectance. Traditional sparse hyperspectral unmixing often leads to an ill-posed inverse problem, which can be circumvented by spatial regularization approaches. However, their adoption has come at the expense of a massive increase in computational cost. In this paper, a novel multiscale hierarchical model for a method of sparse hyperspectral unmixing is proposed. The paper decomposes HU into two domain problems, one is in an approximation scale representation based on resampling the method’s domain, and the other is in the original domain. The use of multiscale spatial resampling methods for HU leads to an effective strategy that deals with spectral variability and computational cost. Furthermore, the hierarchical strategy with abundant sparsity representation in each layer aims to obtain the global optimal solution. Both simulations and real hyperspectral data experiments show that the proposed method outperforms previous methods in endmember extraction and abundance fraction estimation, and promotes piecewise homogeneity in the estimated abundance without compromising sharp discontinuities among neighboring pixels. Additionally, compared with total variation regularization, the proposed method reduces the computational time effectively.

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“…These methods map the data to a feature space of higher dimension where the mapped data can be represented with a linear model in this space [26]. It is important to note that finding the explicit mapping is however bypassed via the kernel trick [27][28][29][30][31]. In [32,33], the authors proposed a model consisting of a linear mixture and a nonlinear fluctuation.…”

mentioning

confidence: 99%

“…This algorithm is characterized by the trade-off parameter λ NDU , fitting parameter µ NDU and penalty parameter ρ NDU . •The improved incremental KNMF (IIKNMF)[31]: This method extends KNMF by introducing partition matrix theory and considering the relationships among dividing blocks. The incremental KNMF (IKNMF) is proposed to reduce the computing requirements for large-scale data and IIKNMF aims to further improve the abundance results.…”

mentioning

confidence: 99%

Kernel-Based Nonlinear Spectral Unmixing with Dictionary Pruning

2019

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Spectral unmixing extracts subpixel information by decomposing observed pixel spectra into a collection of constituent spectra signatures and their associated fractions. Considering the restriction of linear unmixing model, nonlinear unmixing algorithms find their applications in complex scenes. Kernel-based algorithms serve as important candidates for nonlinear unmixing as they do not require specific model assumption and have moderate computational complexity. In this paper we focus on the linear mixture and nonlinear fluctuation model. We propose a two-step kernel-based unmixing algorithm to address the case where a large spectral library is used as the candidate endmembers or the sparse mixture case. The sparsity-inducing regularization is introduced to perform the endmember selection and the candidate library is then pruned to provide more accurate results. Experimental results with synthetic and real data, particularly those laboratory-created labeled, show the effectiveness of the proposed algorithm compared with state-of-art methods.

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Hyperspectral Unmixing Based on Incremental Kernel Nonnegative Matrix Factorization

Cited by 19 publications

References 24 publications

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

A Multiscale Hierarchical Model for Sparse Hyperspectral Unmixing

Kernel-Based Nonlinear Spectral Unmixing with Dictionary Pruning

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