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

Annala, Leevi (2020). Convolutional neural networks and stochastic modelling in hyperspectral data analysis. JYU dissertations, 332. Jyväskylä: Jyväskylän yliopisto. 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

Open Access: Publication published in an open access channel

Abstract

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