Correntropy-Based Spatial-Spectral Robust Sparsity-Regularized Hyperspectral Unmixing

Li, Xiaorun; Huang, Risheng; Zhao, Liaoying

doi:10.1109/tgrs.2020.2999936

Cited by 16 publications

(8 citation statements)

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“…Correntropy-based Spatial-spectral Robust Unmixing Model (CSsRS) (Li X. et al, 2021) is an unmixing model that uses correntropy-based nonnegative matrix factorization, loss function, and a sparsity penalty. The algorithm is tested using a synthetic dataset created from USGS spectral library (Kokaly et al, 2017) and real datasets: Jasper Ridge (Zhu et al, 2014b), and Urban (Zhu et al, 2014a).…”

Section: Nonnegative Matrix Factorizationmentioning

confidence: 99%

Benchmark for Hyperspectral Unmixing Algorithm Evaluation

Paura,

Marcinkevičius

2023

Informatica

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Over the past decades, many methods have been proposed to solve the linear or nonlinear mixing of spectra inside the hyperspectral data. Due to a relatively low spatial resolution of hyperspectral imaging, each image pixel may contain spectra from multiple materials. In turn, hyperspectral unmixing is finding these materials and their abundances. A few main approaches to performing hyperspectral unmixing have emerged, such as nonnegative matrix factorization (NMF), linear mixture modelling (LMM), and, most recently, autoencoder networks. These methods use different approaches in finding the endmember and abundance of information from hyperspectral images. However, due to the huge variation of hyperspectral data being used, it is difficult to determine which methods perform sufficiently on which datasets and if they can generalize on any input data to solve hyperspectral unmixing problems. By trying to mitigate this problem, we propose a hyperspectral unmixing algorithm testing methodology and create a standard benchmark to test already available and newly created algorithms. A few different experiments were created, and a variety of hyperspectral datasets in this benchmark were used to compare openly available algorithms and to determine the best-performing ones.

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Section: Nonnegative Matrix Factorizationmentioning

confidence: 99%

Benchmark for Hyperspectral Unmixing Algorithm Evaluation

Paura,

Marcinkevičius

2023

Informatica

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“…where W is the curvelet transform basis. Very recently, correntropy-based adaptive sparsity constraint [94] was imposed to abundances for each pixel. Besides, a generalized minimax concave (GMC) sparsity regularizer was embedded into NMF [95], which is nonconvex and nonseparable, avoiding systematic underestimation of high components of sparse vector and producing more accurate sparse approximation.…”

Section: B Sparsity Constraintsmentioning

confidence: 99%

“…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%

“…For example, a correntropy based NMF (CENMF) [128] was proposed to suppress the influence of noisy bands efficiently. 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].…”

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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“…In most cases, it is assumed that the intensity of Gaussian noise in each band of HSIs is the same. However, the Gaussian noise is usually caused by sensor noise or dark current variation [21], and its intensity varies from band to band [22]. In addition, various mixed noises, such as impulse noise and deadlines [23].…”

Section: Introductionmentioning

confidence: 99%

Bandwise Model Based on Spectral Prior Information for Sparse Unmixing

Kang

Jiang

et al. 2021

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

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The purpose of sparse unmixing (SU) is to find the optimal spectral subset from the spectral library and uses this subset to model each pixel in the hyperspectral data. The existing SU methods concern Gaussian noise a lot and focus less on the varied intensity of Gaussian noise in different bands and other types of noise, e.g., impulse noise and deadlines. Besides, the high coherence of the spectral library limits the performance of SU. Given the above problems, this paper proposes a new method, called Bandwise Model based on Spectral Prior Information (BMSPI). This proposed BMSPI models the Gaussian noise across different spectral bands and the other types of mixed noise under the maximum a posteriori probability framework, and decreases the effect of high coherence in the spectral library with the spectral prior information. The Alternating Direction Method of Multipliers (ADMM) is adopted to solve the BMSPI. The results of the simulated and real data experiments show that the bandwise model can suppress noises of different types effectively, and the spectral prior information is conducive to guide SU. The advantage of BMSPI is that the mentioned information is used completely. Thus, the accuracy of abundance estimation is improved.

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Correntropy-Based Spatial-Spectral Robust Sparsity-Regularized Hyperspectral Unmixing

Cited by 16 publications

References 50 publications

Benchmark for Hyperspectral Unmixing Algorithm Evaluation

Benchmark for Hyperspectral Unmixing Algorithm Evaluation

Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review

Bandwise Model Based on Spectral Prior Information for Sparse Unmixing

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