Deep learning architectures for hyperspectral imaging applications
Abstract
A typical consumer camera captures three bands of light corresponding to red,
green and blue colors. A hyperspectral imager captures dozens or even hundreds
of bands. A depth sensing camera captures the distance to the target at
each pixel. Imaging spectra and depth opens new possibilities for extracting information
about the target, and these kind of imagers have already been used
in applications in agriculture, astronomy, forestry, medical imaging and other industries.
The captured high-dimensional data volumes are large, and extracting
meaningful information from them requires advanced and efficient processing
methods. Previously, the need for expert manual work has limited the utilization
of data in large scale. This research introduces neural network models for solving
these problems in a few case applications. It also demonstrates hyperspectral
measurement methods: one for radiance approximation and another for angular
reflectance measurement by combining a depth camera with a hyperspectral
camera.
Main Author
Format
Theses
Doctoral thesis
Published
2023
Series
ISBN
978-951-39-9299-6
Publisher
Jyväskylän yliopisto
The permanent address of the publication
https://urn.fi/URN:ISBN:978-951-39-9299-6Käytä tätä linkitykseen.
ISSN
2489-9003
Language
English
Published in
JYU Dissertations
Contains publications
- Artikkeli I: 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 (Article 108510B). SPIE, The International Society for Optical Engineering. SPIE conference proceedings, 10851. DOI: 10.1117/12.2509871. JYX: jyx.jyu.fi/handle/123456789/63888
- Artikkeli II: Pölönen, I., Annala, L., Rahkonen, S., Nevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., & 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. JYX: jyx.jyu.fi/handle/123456789/73412
- Artikkeli III: Rahkonen, S., Koskinen, E., Pölönen, I., Heinonen, T., Ylikomi, T., Äyrämö, S., & Eskelinen, M. A. (2020). Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks. Journal of Medical Imaging, 7(2), Article 024001. DOI: 10.1117/1.JMI.7.2.024001
- Artikkeli IV: Rahkonen, S. and Pölönen, I. (2023). Method for radiance approximation of hyperspectral data using deep neural network. Impact of scientific computing on science and society. Springer. Pending publication.
- Artikkeli V: Rahkonen, S., Lind, L., Raita-Hakola, A.-M., Kiiskinen, S., & Pölönen, I. (2022). Reflectance Measurement Method Based on Sensor Fusion of Frame-Based Hyperspectral Imager and Time-of-Flight Depth Camera. Sensors, 22(22), Article 8668. DOI: 10.3390/s22228668
License
Copyright© The Author & University of Jyväskylä