Computational methods for hyperspectral imaging using Fabry–Perot interferometers and colour cameras
Abstract
Recent research into new technologies for hyperspectral imaging has produced
small imagers capable of very fast capture of spectral and spatial information. A
design based on an electronically tunable Fabry–Perot interferometer combined
with existing camera technology has been developed by VTT and is being utilized
in novel applications, such as drone based and handheld hyperspectral imaging.
The design allows very fast capture of hyperspectral image cubes with great spatial
resolution using either a monochromatic or a colour filter array image sensor. The
latter allows imaging speed and wavelength range to be further extended by
computing multiple narrowband images from a single exposure.
This research describes the process of computing spectroscopic data using
these types of imagers and introduces software tools developed by the author
for this purpose. The included articles present solutions developed during the
research for building analysis software for hyperspectral imaging using high
level languages. They also document computational challenges that need to
be considered when utilizing colour filter arrays for hyperspectral imaging and
demonstrate the feasibility of this type of imager for use in drone based imaging
and laboratory conditions. The software libraries produced during the research
are made publicly available under free licenses to facilitate development of new
hyperspectral imaging applications using this technology.
Keywords: Hyperspectral imaging, colour filter array, Fabry–Perot interferometer,
software development, data analysis, machine learning
Main Author
Format
Theses
Doctoral thesis
Published
2019
Series
ISBN
978-951-39-7967-6
Publisher
Jyväskylän yliopisto
The permanent address of the publication
https://urn.fi/URN:ISBN:978-951-39-7967-6Use this for linking
ISSN
2489-9003
Language
English
Published in
JYU Dissertations
Contains publications
- Artikkeli I: Honkavaara, E., Eskelinen, M., Pölönen, I., Saari, H., Ojanen, H., Mannila, R., . . . Pulkkanen, M. (2016). Remote Sensing of 3-D Geometry and Surface Moisture of a Peat Production Area Using Hyperspectral Frame Cameras in Visible to Short-Wave Infrared Spectral Ranges Onboard a Small Unmanned Airborne Vehicle (UAV). IEEE Transactions on Geoscience and Remote Sensing, 54 (9). DOI: 10.1109/TGRS.2016.2565471
- Artikkeli II: Eskelinen, M. (2017). Software Framework for Hyperspectral Data Exploration and Processing in MATLAB. In E. Honkavaara, B. Hu, K. Karantzalos, X. Liang, R. Müller, E. Nocerino, . . . , & P. Rönnholm (Eds.), ISPRS SPEC3D 2017 : Frontiers in Spectral imaging and 3D Technologies for Geospatial Solutions (pp. 47-50). International Society for Photogrammetry and Remote Sensing. DOI: 10.5194/isprs-archives-XLII-3-W3-47-2017
- Artikkeli III: Annala, L., Eskelinen, M., Hämäläinen, J., Riihinen, A., & Pölönen, I. (2018). Practical Approach for Hyperspectral Image Processing in Python. In J. Jiang, A. Shaker, H. Zhang, X. Liang, B. Osmanoglu, U. Soergel, . . . K. Komp (Eds.), ISPRS TC III Mid-term Symposium “Developments, Technologies and Applications in Remote Sensing” (pp. 45-52). International Society for Photogrammetry and Remote Sensing. DOI: 10.5194/isprs-archives-XLII-3-45-2018
- Artikkeli IV: Trops, R., Hakola, A.-M., Jääskeläinen, S., Näsilä, A., Annala, L., Eskelinen, M., . . . Rissanen, A. (2019). Miniature MOEMS hyperspectral imager with versatile analysis tools. In W. Piyawattanametha, Y.-H. Park, & H. Zappe (Eds.), Proceedings of SPIE Volume 10931 : MOEMS and Miniaturized Systems XVIII; 109310W (pp. 109310W). SPIE, The International Society for Optical Engineering. DOI: 10.1117/12.2506366
- Artikkeli V: Eskelinen, M., & Hämäläinen, J. (2019). Dangers of Demosaicing : Confusion From Correlation. In WHISPERS 2018 : 9th Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. IEEE. DOI: 10.1109/WHISPERS.2018.8747204
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