0618211 - ÚPT 2025 RIV US eng C - Conference Paper (international conference)
Vaculík, Ondřej - Šerý, Mojmír - Šilhan, Lukáš - Šilhanová, Denisa - Zemánek, Pavel
Addressing Data Management for Custom Built Hyperspectral Microscopy Station.
2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). New York: IEEE, 2024, č. článku 10876463. ISBN 979-8-3315-1313-9. E-ISSN 2158-6276.
[Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing /14./. Helsinki (FI), 09.12.2024-11.12.2024]
R&D Projects: GA MŠMT(CZ) EH22_008/0004624
Institutional support: RVO:68081731
Keywords : pushbroom hyperspectral imaging * di-mensionality reduction * image compression * machine learning * data management
OECD category: Optics (including laser optics and quantum optics)
Result website:
https://ieeexplore.ieee.org/document/10876463DOI: https://doi.org/10.1109/WHISPERS65427.2024.10876463

Hyperspectral (HS) imaging, originally developed for satellite applications as a remote sensing spatiospectral analysis technique, has been adopted by numerous scientific fields [1], [2]. By combining HS imaging and microscopy, we can capture not only spectral and spatial information but also non-destructively uncover compositional and chemical information, which is especially beneficial for applications in agriculture [3], food safety and inspection [4], medical research [5], microbiology [6] and algae research [7]. To assist in this endeavor, we have developed a modular pushbroom HS microscope platform that allows automated HS measurement to be conducted. This naturally poses a problem in the form of proper data management, as the volume and data storage requirements for such a type of system are essential to plan for in advance. In this paper, we address this problem and present our HS data management workflow with complete data and metadata collection, suitable format selection, and data compression, all with the FAIR [8] data principles in mind.
Permanent Link: https://hdl.handle.net/11104/0365023