Computational Methods in Spectral Imaging
Abstract
Spectral imaging is an evolving technology with numerous applications. These images can be computationally processed in several ways. In addition to machine learning methods, spectral images can be processed mathematically by modelling or by combining both approaches. This chapter looks at spectral imaging and the computational methods commonly used in it. We review methods related to preprocessing, modelling, and machine learning, and become familiar with some applications.
Main Author
Format
Books
Book part
Published
2023
Series
Subjects
Publication in research information system
Publisher
Springer
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202401091103Use this for linking
Parent publication ISBN
978-3-031-29081-7
Review status
Peer reviewed
ISSN
1871-3033
DOI
https://doi.org/10.1007/978-3-031-29082-4_17
Language
English
Published in
Computational Methods in Applied Sciences
Is part of publication
Impact of Scientific Computing on Science and Society
Citation
- Pölönen, I. (2023). Computational Methods in Spectral Imaging. In P. Neittaanmäki, & M.-L. Rantalainen (Eds.), Impact of Scientific Computing on Science and Society (pp. 295-313). Springer. Computational Methods in Applied Sciences, 58. https://doi.org/10.1007/978-3-031-29082-4_17
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Copyright© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG