Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing

Guan, Qiang; Xu, Tao; Feng, Shuai; Yu, Fenghua; Song, Kai

doi:10.1080/22797254.2022.2125447

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…A range of cutting-edge data processing techniques has particularly been applied in the CH domain to reduce the dimensionality of the dataset, classify and unmix spectral signature to map paint components. Thus, numerous approaches were developed, starting from the conventional multivariate analyses and statistical methods (e.g., spectral angle mapper (SAM) [10][11][12][13], fully constrained least square (FCLS) [14][15][16], principal component analyses (PCA) [17][18][19][20], minimum noise fraction transform (MNF) [21,22], and k-means clustering [23][24][25]), to the more advanced machine learning algorithms (support vector machine (SVM) [26,27], hierarchical clustering [28], embedding techniques [29][30][31][32], MaxD [11,24,33], dictionary learning [34,35]) with a growing interest for neural network algorithms (NNs) [36]. NN-based models first gained a tremendous rise in digital image classification due to their superior ability in feature extraction and pattern recognition [37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

Liu

Miteva

Delnevo

et al. 2023

Sensors

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Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method.

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Section: Introductionmentioning

confidence: 99%

Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

Liu

Miteva

Delnevo

et al. 2023

Sensors

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GAUSS: Guided encoder - decoder Architecture for hyperspectral Unmixing with Spatial Smoothness

Wickramathilaka,

Fernando,

Jayasundara

et al. 2023

European Journal of Remote Sensing

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Spatial Validation of Spectral Unmixing Results: A Systematic Review

Cavalli

2023

Remote Sensing

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The pixels of remote images often contain more than one distinct material (mixed pixels), and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared effort has enabled the development of techniques for retrieving information from mixed pixels. The most analyzed, implemented, and employed procedure is spectral unmixing. Among the extensive literature on the spectral unmixing, nineteen reviews were identified, and each highlighted the many shortcomings of spatial validation. Although an overview of the approaches used to spatially validate could be very helpful in overcoming its shortcomings, a review of them was never provided. Therefore, this systematic review provides an updated overview of the approaches used, analyzing the papers that were published in 2022, 2021, and 2020, and a dated overview, analyzing the papers that were published not only in 2011and 2010, but also in 1996 and 1995. The key criterion is that the results of the spectral unmixing were spatially validated. The Web of Science and Scopus databases were searched, using all the names that were assigned to spectral unmixing as keywords. A total of 454 eligible papers were included in this systematic review. Their analysis revealed that six key issues in spatial validation were considered and differently addressed: the number of validated endmembers; sample sizes and sampling designs of the reference data; sources of the reference data; the creation of reference fractional abundance maps; the validation of the reference data with other reference data; the minimization and evaluation of the errors in co-localization and spatial resampling. Since addressing these key issues enabled the authors to overcome some of the shortcomings of spatial validation, it is recommended that all these key issues be addressed together. However, few authors addressed all the key issues together, and many authors did not specify the spatial validation approach used or did not adequately explain the methods employed.

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Optimal segmentation and improved abundance estimation for superpixel-based Hyperspectral Unmixing

Cited by 3 publications

References 40 publications

Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

GAUSS: Guided encoder - decoder Architecture for hyperspectral Unmixing with Spatial Smoothness

Spatial Validation of Spectral Unmixing Results: A Systematic Review

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