Sparsity-Constrained Distributed Unmixing of Hyperspectral Data

Khoshsokhan, Sara; Rajabi, Roozbeh; Zayyani, Hadi

doi:10.1109/jstars.2019.2901122

Cited by 19 publications

(9 citation statements)

References 55 publications

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“…Endmember extraction can be accomplished using the methods based on projection convex geometry analysis, sparse matrix regression analysis, or statistical learning analysis and so on [20,21]. There are cases, paintings for example, where the images may contain less or even no pure pixel but mixed pixels.…”

Section: Endmember Extractionmentioning

confidence: 99%

Quantitative analysis of mixed pigments for Chinese paintings using the improved method of ratio spectra derivative spectrophotometry based on mode

et al. 2020

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Background: Mixed pigment analysis is an important and complex subject in preserving and restoring Chinese paintings since the colors observed by our naked eyes or instruments such as hyperspectral cameras are usually a mixture of several kinds of pigments. The purpose of this study was to explore a more effective method to confirm the type of every pure pigment and their proportion in pigment mixtures on the surface of paintings. Methods: Two endmember extraction algorithms were adopted to identify the types of pigments and an improved method of ratio spectra derivative spectrophotometry was used to determine their proportion. Main works: (1) Extracting the pure pigment components from mixed spectrum by adopting two blind source separation algorithms: Independent Component Analysis and Non-negative Matrix Factorization; (2) matching the separated pure spectrum with the pigment spectral library built in our laboratory to determine the pigment type; and (3) calculating the proportions of mixed pigments using the Ratio Spectra Derivative Spectrophotometry based on Mode, which is improved based on the original algorithm. Finally, a comparison was made with two abundance inversion algorithms: Least Squares Algorithm and Minimum Volume Simplex Analysis. The correlation coefficient and root mean square error were used to provide evidence for accuracy evaluation. Conclusions: (1) Non-negative matrix factorization is more suitable for endmember extraction; and (2) Ratio spectra derivative spectrophotometry based on mode is more suitable for abundance inversion.

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Section: Endmember Extractionmentioning

confidence: 99%

Quantitative analysis of mixed pigments for Chinese paintings using the improved method of ratio spectra derivative spectrophotometry based on mode

et al. 2020

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“…Apply the abundance non-negativity and sum-to-one constraints to provide fully constrained fractions. For this part, we project the output fractions onto the canonical simplex [56], as presented in [38] and [57].…”

Section: Methodological Frameworkmentioning

confidence: 99%

Spatially Enhanced Spectral Unmixing Through Data Fusion of Spectral and Visible Images from Different Sensors

Kizel

Benediktsson

2020

Remote Sensing

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We propose an unmixing framework for enhancing endmember fraction maps using a combination of spectral and visible images. The new method, data fusion through spatial information-aided learning (DFuSIAL), is based on a learning process for the fusion of a multispectral image of low spatial resolution and a visible RGB image of high spatial resolution. Unlike commonly used methods, DFuSIAL allows for fusing data from different sensors. To achieve this objective, we apply a learning process using automatically extracted invariant points, which are assumed to have the same land cover type in both images. First, we estimate the fraction maps of a set of endmembers for the spectral image. Then, we train a spatial-features aided neural network (SFFAN) to learn the relationship between the fractions, the visible bands, and rotation-invariant spatial features for learning (RISFLs) that we extract from the RGB image. Our experiments show that the proposed DFuSIAL method obtains fraction maps with significantly enhanced spatial resolution and an average mean absolute error between 2% and 4% compared to the reference ground truth. Furthermore, it is shown that the proposed method is preferable to other examined state-of-the-art methods, especially when data is obtained from different instruments and in cases with missing-data pixels.

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“…Compute the estimated active set I k with one of the de nitions (AS1) or (AS2). Compute the search direction of the active and inactive variables with (15) and (16).…”

Section: Step 1 (Computation Of the Search Direction)mentioning

confidence: 99%

“…To produce useful decompositions, some constraints on the scales of U or V are needed. Hoyer proposed the nonnegative sparse coding in [10,11], and NMF with sparse constraint had been applied to unmix hyperspectral data [12][13][14][15][16]. Cai et al presented graph-regularized nonnegative matrix factorization (GNMF) [17,18].…”

Section: Introductionmentioning

confidence: 99%

Active Set Type Algorithms for Nonnegative Matrix Factorization in Hyperspectral Unmixing

Sun

Han

Liu

2019

Mathematical Problems in Engineering

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Hyperspectral unmixing is a powerful method of the remote sensing image mining that identifies the constituent materials and estimates the corresponding fractions from the mixture. We consider the application of nonnegative matrix factorization (NMF) for the mining and analysis of spectral data. In this paper, we develop two effective active set type NMF algorithms for hyperspectral unmixing. Because the factor matrices used in unmixing have sparse features, the active set strategy helps reduce the computational cost. ese active set type algorithms for NMF is based on an alternating nonnegative constrained least squares (ANLS) and achieve a quadratic convergence rate under the reasonable assumptions. Finally, numerical tests demonstrate that these algorithms work well and that the function values decrease faster than those obtained with other algorithms.

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Sparsity-Constrained Distributed Unmixing of Hyperspectral Data

Cited by 19 publications

References 55 publications

Quantitative analysis of mixed pigments for Chinese paintings using the improved method of ratio spectra derivative spectrophotometry based on mode

Quantitative analysis of mixed pigments for Chinese paintings using the improved method of ratio spectra derivative spectrophotometry based on mode

Spatially Enhanced Spectral Unmixing Through Data Fusion of Spectral and Visible Images from Different Sensors

Active Set Type Algorithms for Nonnegative Matrix Factorization in Hyperspectral Unmixing

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