Convolutional neural networks and stochastic modelling in hyperspectral data analysis
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: Hyperspectral imaging, Convolutional neural network, Stochastic modelling,
Biophysical parameter retrieval, Data augmentation
Main Author
Format
Theses
Doctoral thesis
Published
2020
Series
ISBN
978-951-39-8453-3
Publisher
Jyväskylän yliopisto
The permanent address of the publication
https://urn.fi/URN:ISBN:978-951-39-8453-3Käytä tätä linkitykseen.
ISSN
2489-9003
Language
English
Published in
JYU Dissertations
Contains publications
- Artikkeli I: 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 II: Pölönen, I., Annala, L., Rahkonen, S., Nevalainen, O., Honkavaara, E., Tuominen, S., . . . , & Hakala, T. (2019). Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network. In WHISPERS 2018 : 9th Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. IEEE. DOI: 10.1109/WHISPERS.2018.8747253
- Artikkeli III: Pölönen, I., Rahkonen, S., Annala, L., & Neittaanmäki, N. (2019). Convolutional neural networks in skin cancer detection using spatial and spectral domain. In B. Choi, & H. Zeng (Eds.), Proceedings of SPIE Volume 10851 : Photonics in Dermatology and Plastic Surgery 2019 (pp. 108510B). SPIE, The International Society for Optical Engineering. DOI: 10.1117/12.2509871
- Artikkeli IV: Annala, Leevi; Honkavaara, Eija; Tuominen, Sakari; Pölönen, Ilkka (2020). Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion. Remote Sensing, 12 (2), 283. DOI: 10.3390/rs12020283
- Artikkeli V: Annala, Leevi and Pölönen, Ilkka. (2020). Kubelka-Munk Model and Stochastic Model Comparison in Skin Physical Parameter Retrieval. Computational Sciences and Artificial Intelligence in Industry – New digital technologies for solving future societal and economical challenges. In press.
- Artikkeli VI: Annala, Leevi; Äyrämö, Sami; Pölönen, Ilkka (2020). Comparison of Machine Learning Methods in Stochastic Skin Optical Model Inversion. Applied Sciences, 10 (20), 7097. DOI: 10.3390/app10207097
- Artikkeli VII: Annala, Leevi; Neittaanmäki, Noora; Paoli, John; Zaar, Oscar; Pölönen, Ilkka (2020). Generating Hyperspectral Skin Cancer Imagery using Generative Adversarial Neural Network. In EMBC 2020 : Proceedings of the 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 1600-1603. DOI: 10.1109/EMBC44109.2020.9176292
License
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