Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
Wolfmayr, M. (2023). Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging. Applied Sciences, 13(16), Article 9373. https://doi.org/10.3390/app13169373
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Applied SciencesAuthors
Date
2023Discipline
Laskennallinen tiedeComputing, Information Technology and MathematicsComputational ScienceComputing, Information Technology and MathematicsCopyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland.
An approach to parameter optimization for the low-rank matrix recovery method in hyperspectral imaging is discussed. We formulate an optimization problem with respect to the initial parameters of the low-rank matrix recovery method. The performance for different parameter settings is compared in terms of computational times and memory. The results are evaluated by computing the peak signal-to-noise ratio as a quantitative measure. The potential improvement in the performance of the noise reduction method is discussed when optimizing the choice of the initial values. The optimization method is tested on standard and openly available hyperspectral data sets, including Indian Pines, Pavia Centre, and Pavia University.
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https://converis.jyu.fi/converis/portal/detail/Publication/184245521
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Council of Tampere RegionFunding program(s)
ERDF European Regional Development Fund, React-EUAdditional information about funding
This research was funded by the Regional Council of Central Finland/Council of Tampere Region and European Regional Development Fund as part of the coADDVA—ADDing VAlue by Computing in Manufacturing projects of Jamk University of Applied Sciences.License
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