Dr. Ilkka Pölönen Profile

Dr. Ilkka Pölönen

Researcher at Univ of Jyvaskylä

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Author

Publications (16)

SPIE Journal Paper | 12 September 2023 Open Access

HyperBlend leaf simulator: improvements on simulation speed, generalizability, and parameterization

Kimmo A. Riihiaho, Leevi Lind, Ilkka Pölönen

JARS, Vol. 17, Issue 03, 038505, (September 2023) https://doi.org/10.1117/12.10.1117/1.JRS.17.038505

KEYWORDS: Education and training, Reflectivity, Transmittance, Hyperspectral imaging, Scattering, Error analysis, Statistical modeling, Particles, Hyperspectral simulation, Computer simulations

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Proceedings Article | 5 October 2021 Presentation + Paper

Noise reduction in asteroid imaging using a miniaturized spectral imager

Monika Wolfmayr, Ilkka Pölönen, Leevi Lind, Tomáš Kašpárek, Antti Penttilä, Tomáš Kohout

Proceedings Volume 11858, 118580P (2021) https://doi.org/10.1117/12.2600201

KEYWORDS: Fabry–Perot interferometers, Denoising, Imaging systems, Signal to noise ratio, Asteroids, Image quality, Digital filtering, Hyperspectral imaging, Image filtering, Wavelets, Gaussian filters

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In October 2024, European Space Agency’s Hera mission will be launched, targeting the binary asteroid Didymos. Hera will host the Juventas and Milani CubeSats, the first CubeSats to orbit close to a small celestial body performing scientific and technological operations. The primary scientific payload of the Milani CubeSat is the SWIR, NIR, and VIS imaging spectrometer ASPECT. The Milani mission objectives include mapping the global composition and the characterization of the binary asteroid surface. Onboard data processing and evaluation steps will be applied due to the limited data budget for the downlink to Earth and to perform the technological demonstration of a novel semi-autonomous hyperspectral imaging mission. Before downloading, the image data is evaluated in terms of sharpness and coverage and processed by compression. The challenges and their proposed solutions for the data processing part of the mission are investigated through studies. Since most noise contributors are unknown until Milani is activated, different noises are studied based on previous missions and derived from hyperspectral images taken in a laboratory environment mimicking the real-life situation. The hyperspectral camera technology in the laboratory is similar to the one used in the ASPECT imager payload. Both ASPECT and the imagers utilized in our measurements are based on employing a Fabry-Pérot interferometer as an adjustable transmission filter. The imagers are also designed and built by the same party, the Technical Research Centre of Finland (VTT). Best performing denoising techniques for each noise type are discussed on the one hand for the entire datacubes and on the other hand for the spatial domain only since the mission includes images taken only at specific wavebands. The advantage of applying denoising for the whole datacube comes from the internal dependencies between the wavebands, allowing efficient processing. A trade-off study for several noise reduction algorithms is presented. The goal is to implement efficient image processing algorithms with low computational complexity, securing the successful execution of the mission.

Proceedings Article | 12 September 2021 Presentation + Paper

Hyperspectral imaging of asteroids using an FPI-based sensor

Leevi Lind, Hannu Laamanen, Ilkka Pölönen

Proceedings Volume 11858, 118580F (2021) https://doi.org/10.1117/12.2599514

KEYWORDS: Reflectivity, Asteroids, Imaging systems, Hyperspectral imaging, Calibration, Sensors, Sun, Space operations, Fabry–Perot interferometers, Imaging spectroscopy, Reflectance spectroscopy

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Proceedings Article | 23 September 2020 Presentation + Paper

Minimal learning machine in hyperspectral imaging classification

Anna-Maria Hakola, Ilkka Pölönen

Proceedings Volume 11533, 115330R (2020) https://doi.org/10.1117/12.2573578

KEYWORDS: Hyperspectral imaging, Machine learning, Neural networks, Matrices, Data modeling, Optimization (mathematics)

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SPIE Journal Paper | 30 April 2020 Open Access

Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks (Erratum)

Samuli Rahkonen, Emilia Koskinen, Ilkka Pölönen, Tuula Heinonen, Timo Ylikomi, Sami Äyrämö, Matti Eskelinen

JMI, Vol. 7, Issue 02, 029801, (April 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.2.029801

KEYWORDS: Cancer, Neural networks, Image segmentation, Information technology, Medicine

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SPIE Journal Paper | 7 April 2020 Open Access

Multilabel segmentation of cancer cell culture on vascular structures with deep neural networks

Samuli Rahkonen, Emilia Koskinen, Ilkka Pölönen, Tuula Heinonen, Timo Ylikomi, Sami Äyrämö, Matti Eskelinen

JMI, Vol. 7, Issue 02, 024001, (April 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.2.024001

KEYWORDS: Image segmentation, Cancer, Neural networks, RGB color model, Tumor growth modeling, Microscopes, FDA class I medical device development, In vitro testing, Data modeling, Image classification

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Proceedings Article | 26 February 2019 Presentation + Paper

Convolutional neural networks in skin cancer detection using spatial and spectral domain

Ilkka Pölönen, Samuli Rahkonen, Leevi Annala, Noora Neittaanmäki

Proceedings Volume 10851, 108510B (2019) https://doi.org/10.1117/12.2509871

KEYWORDS: Melanoma, Convolutional neural networks, Skin cancer, Imaging systems, Convolution, Hyperspectral imaging, Neural networks, Data modeling, Skin, Tumors

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Proceedings Article | 10 October 2014 Paper

UAV-based hyperspectral monitoring of small freshwater area

I. Pölönen, H.-H. Puupponen, Eija Honkavaara, A. Lindfors, H. Saari, L. Markelin, T. Hakala, K. Nurminen

Proceedings Volume 9239, 923912 (2014) https://doi.org/10.1117/12.2067422

KEYWORDS: Imaging systems, Unmanned aerial vehicles, Hyperspectral imaging, Target detection, Image sensors, Fabry–Perot interferometry, Cameras, Sensors, Image processing, Detection and tracking algorithms

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Proceedings Article | 10 June 2014 Paper

Detecting explosive substances by the IR spectrography

J. Kuula, Heikki Rinta, I. Pölönen, H.-H. Puupponen, Marko Haukkamäki, T. Teräväinen

Proceedings Volume 9073, 90730Q (2014) https://doi.org/10.1117/12.2050157

KEYWORDS: Explosives, Infrared spectroscopy, Short wave infrared radiation, Mid-IR, Spectroscopy, Cameras, FT-IR spectroscopy, Statistical analysis, Explosives detection, Infrared imaging

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Proceedings Article | 5 June 2014 Paper

The challenges of analysing blood stains with hyperspectral imaging

J. Kuula, H.-H. Puupponen, H. Rinta, I. Pölönen

Proceedings Volume 9112, 91120W (2014) https://doi.org/10.1117/12.2050180

KEYWORDS: Blood, Hyperspectral imaging, Chemical analysis, Biological research, Short wave infrared radiation, Spectroscopy, Statistical analysis, Forensic science, Military medicine, Analytical research

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Proceedings Article | 24 October 2013 Paper

Miniaturized hyperspectral imager calibration and UAV flight campaigns

Heikki Saari, Ilkka Pölönen, Heikki Salo, Eija Honkavaara, Teemu Hakala, Christer Holmlund, Jussi Mäkynen, Rami Mannila, Tapani Antila, Altti Akujärvi

Proceedings Volume 8889, 88891O (2013) https://doi.org/10.1117/12.2028972

KEYWORDS: Imaging systems, Unmanned aerial vehicles, Calibration, Hyperspectral imaging, Sensors, Cameras, Optical filters, Radon, Fabry–Perot interferometry, Image sensors

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Proceedings Article | 16 October 2013 Paper

A case study of a precision fertilizer application task generation for wheat based on classified hyperspectral data from UAV combined with farm history data

Jere Kaivosoja, Liisa Pesonen, Jouko Kleemola, Ilkka Pölönen, Heikki Salo, Eija Honkavaara, Heikki Saari, Jussi Mäkynen, Ari Rajala

Proceedings Volume 8887, 88870H (2013) https://doi.org/10.1117/12.2029165

KEYWORDS: Nitrogen, Unmanned aerial vehicles, Agriculture, Raster graphics, Remote sensing, Hyperspectral imaging, Visualization, Clouds, Biological research, Satellites

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Proceedings Article | 16 October 2013 Paper

Hyperspectral imaging based biomass and nitrogen content estimations from light-weight UAV

I. Pölönen, H. Saari, J. Kaivosoja, E. Honkavaara, L. Pesonen

Proceedings Volume 8887, 88870J (2013) https://doi.org/10.1117/12.2028624

KEYWORDS: Hyperspectral imaging, Nitrogen, Biological research, Unmanned aerial vehicles, Imaging systems, Feature extraction, Electroluminescent displays, Vegetation, Data analysis, Agriculture

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Proceedings Article | 18 June 2012 Paper

Using VIS/NIR and IR spectral cameras for detecting and separating crime scene details

Jaana Kuula, Ilkka Pölönen, Hannu-Heikki Puupponen, Tuomas Selander, Tapani Reinikainen, Tapani Kalenius, Heikki Saari

Proceedings Volume 8359, 83590P (2012) https://doi.org/10.1117/12.918555

KEYWORDS: Cameras, Blood, Inspection, Short wave infrared radiation, Forensic science, Imaging spectroscopy, Infrared cameras, Eye, Infrared imaging, Statistical analysis

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Proceedings Article | 12 May 2012 Paper

Methods for estimating forest stem volumes by tree species using digital surface model and CIR images taken from light UAS

Heikki Salo, Ville Tirronen, Ilkka Pölönen, Sakari Tuominen, Andras Balazs, Jan Heikkilä, Heikki Saari

Proceedings Volume 8390, 83900G (2012) https://doi.org/10.1117/12.919085

KEYWORDS: Remote sensing, Unmanned aerial vehicles, Hyperspectral imaging, Cameras, Electroluminescent displays, LIDAR, Image processing, Photogrammetry, Imaging systems, Prototyping

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Proceedings Article | 5 May 2012 Paper

Biomass estimator for NIR image with a few additional spectral band images taken from light UAS

Ilkka Pölönen, Heikki Salo, Heikki Saari, Jere Kaivosoja, Liisa Pesonen, Eija Honkavaara

Proceedings Volume 8369, 836905 (2012) https://doi.org/10.1117/12.918551

KEYWORDS: Biological research, Near infrared, Hyperspectral imaging, Diffusion, Cameras, Unmanned aerial vehicles, Clouds, Imaging systems, Image analysis, Image processing

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Showing 5 of 16 publications

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