G5 Doctoral dissertation (article)
Convolutional neural networks and stochastic modelling in hyperspectral data analysis (2020)
Konvoluutioneuroverkkojen ja stokastisen simuloinnin käyttö hyperspektrikuvien analysoinnissa

Annala, L. (2020). Convolutional neural networks and stochastic modelling in hyperspectral data analysis [Doctoral dissertation]. Jyväskylän yliopisto. JYU dissertations, 332. http://urn.fi/URN:ISBN:978-951-39-8453-3

JYU authors or editors

Publication details

All authors or editors: Annala, Leevi

eISBN: 978-951-39-8453-3

Journal or series: JYU dissertations

eISSN: 2489-9003

Publication year: 2020

Number in series: 332

Number of pages in the book: 1 verkkoaineisto (55 sivua, 62 sivua useina numerointijaksoina, 24 numeroimatonta sivua)

Publisher: Jyväskylän yliopisto

Place of Publication: Jyväskylä

Publication country: Finland

Publication language: English

Persistent website address: http://urn.fi/URN:ISBN:978-951-39-8453-3

Publication open access: Openly available

Publication channel open access: Open Access channel


Hyperspectral imaging is relatively new and rapidly growing field of research. The datasets produced by hyperspectral imaging are large, and handling such data requires large computational resources. Therefore, there is a need for developing machine learning methods that can cope with the data, and methods to reduce the necessary amount of data gathering missions. For the latter, problem the author and his co-authors have developed stochastic modelling and generative adversarial neural networks for data augmentation. In machine learning, they have experimented with using convolutional neural network in conjunction with said stochastic model in order to retrieve useful information from hyperspectral data. Additionally, the author lists useful Python packages for hyperspectral data analysis.

Keywords: spectral imaging; imaging; data mining; machine learning; stochastic processes; neural networks (information technology)

Free keywords: hyperspectral imaging; convolutional neural network; stochastic modelling; biophysical parameter retrieval; data augmentation

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2020

Last updated on 2022-15-06 at 17:43