Correntropy-Based Autoencoder-Like NMF With Total Variation for Hyperspectral Unmixing

Feng, Xin-Ru; Li, Heng-Chao; Liu, Shuang; Zhang, Hongyan

doi:10.1109/lgrs.2020.3020896

Cited by 17 publications

(12 citation statements)

References 21 publications

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“…Hence, the classical NMF model defined by the least-squares loss is sensitive to noise, leading to dramatically degrading the unmixing performance. To improve the robustness of NMF, many models have been reported based on certain metrics, including but not limited to bounded Itakura-Saito (IS) divergence [125], L 2,1 -norm regularizer [62], [113], [126], [127], CIM [90], [94], [128], [129], Cauchy function [130], and general robust loss function [131]. The bounded IS divergence was employed to address the additive, multiplicative, and mixed noises in HSIs [125].…”

Section: Robust Nmfmentioning

confidence: 99%

“…Considering the diversity of the noise levels of pixels, correntropy-based spatial-spectral robust sparsity-regularized NMF (CSsRS-NMF) was proposed in [94] by adaptive assigning weights to noisy pixels. Furthermore, robustness can be achieved from an element-wise noise perspective [90], [129]. By cutting off the large error via the truncation operation, the truncated Cauchy loss [169] exhibits robustness to outliers.…”

Section: Robust Nmfmentioning

confidence: 99%

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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: Robust Nmfmentioning

confidence: 99%

Section: Robust 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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“…Specially, simplex minimum volume, abundance sparsity, and abundance smoothness have been simultaneously studied in the multiplepriors ensemble constrained NMF (MPEC-NMF) model [42]. Many other related methods, such as graph regularized based [43], self-paced based NMF algorithms [44], tensor based [45], [46], and deep learning based unmixing algorithms [47]- [49], hava also been developed. However, these algorithms do not deal well with outliers or noise and the effect on spatial structure of abundance distribution still needs to be further verified.…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Constrained Nonnegative Matrix Factorization for Spectral Mixture Analysis of Hyperspectral Images

Zhang

Mei

Feng

et al. 2021

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

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Hyperspectral Spectral Mixture Analysis (SMA), which intends to decompose mixed pixels into a collection of endmembers weighted by their corresponding fraction abundances, has been successfully used to tackle mixed-pixel problem in hyperspectral remote sensing applications. As an approach of decomposing a high-dimensional data matrix into the multiplication of two nonnegative matrices, Nonnegative Matrix Factorization (NMF) has shown its advantages and been widely applied to SMA. Unfortunately, most of the NMF-based unmixing methods can easily lead to an unsuitable solution, due to inadequate mining of spatial and spectral information and the influence of outliers and noise. To overcome such limitations, a spatial constraint over abundance and a spectral constraint over endmembers are imposed over NMF-based unmixing model for spectral-spatial constrained unmixing. Specifically, a spatial neighborhood preserving constraint is proposed to preserve the local geometric structure of the hyperspectral data by assuming that pixels in a spatial neighborhood generally fall into a low-dimensional manifold, while a minimum spectral distance constraint is formulated to optimize endmember spectra as compact as possible. Furthermore, to handle non-Gaussian noises or outliers, an L 2,1 -norm based loss function is ultimately adopted for the proposed Spectral-Spatial constrained Nonnegative Matrix Factorization (SS-NMF) model and a projected gradient (PG) based optimization algorithm is designed for optimization. Experimental results over both synthetic and realworld datasets demonstrate that the proposed spatial and spectral constraints can certainly improve the performance of NMF-based unmixing and outperform state-of-the-art NMF-based unmixing algorithms.Index Terms-Spectral mixture analysis (SMA), nonnegative matrix factorization (NMF), hyperspectral remote sensing, linear mixture model.

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“…It is unfortunate that the facility restrictions and weather conditions lead to HSI is usually polluted by mixed noise. In addition to limiting the image quality, the existence of noise always hinders the subsequent application, such as, unmixing [4][5][6], classification Y. Chen is with the School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, P. R. China (email: cheny-ong1872008@163.com). [7][8][9], and fusion [10], [11].…”

Section: Introductionmentioning

confidence: 99%

Nonlocal Block-Term Decomposition for Hyperspectral Image Mixed Noise Removal

Zeng

Huang

Chen

et al. 2021

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

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Since the facility restrictions and weather conditions, hyperspectral image (HSI) is generally seriously polluted by a variety of noises. Recently, the method based on block term decomposition with rank-(L, L, 1) (BTD) has attracted wide attention in HSI mixed noise removal. BTD factorizes thirdorder HSI data into the sum of a series of component tensors, where each of the component tensors is represented by the outer product of a rank-L matrix ArB T r and a column vector cr. BTD has clear physical interpretation because its latent factors ArB T r and cr can be interpreted abundance map and spectral signature respectively. The essential uniqueness of BTD is under the lowrank assumption of ArB T r . However, the low-rank assumption is not always held in real scenarios. The BTD-based method usually sets L to full rank to achieve satisfactory results. In this paper, we suggest a novel model based on nonlocal block term decomposition (NLBTD) for HSI mixed noise removal. More specifically, for each grouped similar image block, BTD is introduced to capture nonlocal self-similarity and global spectral lowrankness, the unidirectional total variation (TV) is introduced to preserve local spectral smoothness. By faithfully exploring nonlocal self-similarity, global spectral low-rankness, and local spectral smoothness, the proposed method is expected to produce promising results with guarantee the essential uniqueness of BTD. To tackle the resulting model, we design an efficient algorithm based on the proximal alternating minimization with the theoretical guarantees. Extensive numerical experiments in HSI mixed noise removal demonstrate that the proposed NLBTD method achieves satisfactory performance compared with stateof-the-art methods.

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Correntropy-Based Autoencoder-Like NMF With Total Variation for Hyperspectral Unmixing

Cited by 17 publications

References 21 publications

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

Spectral-Spatial Constrained Nonnegative Matrix Factorization for Spectral Mixture Analysis of Hyperspectral Images

Nonlocal Block-Term Decomposition for Hyperspectral Image Mixed Noise Removal

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