A1 Journal article (refereed)
Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging (2023)

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

JYU authors or editors

Publication details

All authors or editors: Wolfmayr, Monika

Journal or series: Applied Sciences

eISSN: 2076-3417

Publication year: 2023

Publication date: 18/08/2023

Volume: 13

Issue number: 16

Article number: 9373

Publisher: MDPI AG

Publication country: Switzerland

Publication language: English

DOI: https://doi.org/10.3390/app13169373

Publication open access: Openly available

Publication channel open access: Open Access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/88794

Web address of parallel published publication (pre-print): https://arxiv.org/abs/2305.09823

Abstract

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.

Keywords: hyperspectral imaging; optimisation; imaging; spectrometry

Free keywords: noise reduction; nonlinear optimization; low-rank modeling; hyperspectral imaging; signal-to-noise ratio improvement

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2023

JUFO rating: 1