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dc.contributor.authorRahkonen, Samuli
dc.contributor.authorPölönen, Ilkka
dc.contributor.editorNeittaanmäki, Pekka
dc.contributor.editorRantalainen, Marja-Leena
dc.date.accessioned2024-01-09T09:12:45Z
dc.date.available2024-01-09T09:12:45Z
dc.date.issued2023
dc.identifier.citationRahkonen, S., & Pölönen, I. (2023). Method for Radiance Approximation of Hyperspectral Data Using Deep Neural Network. In P. Neittaanmäki, & M.-L. Rantalainen (Eds.), <i>Impact of Scientific Computing on Science and Society</i> (pp. 315-325). Springer. Computational Methods in Applied Sciences, 58. <a href="https://doi.org/10.1007/978-3-031-29082-4_18" target="_blank">https://doi.org/10.1007/978-3-031-29082-4_18</a>
dc.identifier.otherCONVID_183943739
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/92599
dc.description.abstractWe propose a neural network model for calculating the radiance from raw hyperspectral data gathered using a Fabry–Perot interferometer color camera developed by VTT Technical Research Centre of Finland. The hyperspectral camera works by taking multiple images from different wavelength with varying interferometer settings. The raw data needs to be converted to radiance in order to make any use of it, but this leads to larger file sizes. Because of the amount of the data and the structure of the raw data, the processing has to be run in parallel, requiring a lot of memory and time. Using raw camera data could save processing time and file space in applications with computation time requirements. Secondly, this kind of neural network could be used for generating synthetic training data or use it in generative models. The proposed model approaches these problems by combining spatial and spectral-wise convolutions in neural network with minimizing a loss function utilizing the spectral distance and mean squared loss. The used dataset included images from many patients with melanoma skin cancer.en
dc.format.extent450
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofImpact of Scientific Computing on Science and Society
dc.relation.ispartofseriesComputational Methods in Applied Sciences
dc.rightsIn Copyright
dc.subject.otherspektrikuvaus
dc.subject.othertietojenkäsittely
dc.subject.otherspectral imaging
dc.subject.othermodelling
dc.subject.othermachine learning
dc.subject.otherdata processing
dc.titleMethod for Radiance Approximation of Hyperspectral Data Using Deep Neural Network
dc.typebookPart
dc.identifier.urnURN:NBN:fi:jyu-202401091101
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineComputing, Information Technology and Mathematicsfi
dc.contributor.oppiaineComputational Scienceen
dc.contributor.oppiaineComputing, Information Technology and Mathematicsen
dc.type.urihttp://purl.org/eprint/type/BookItem
dc.relation.isbn978-3-031-29081-7
dc.type.coarhttp://purl.org/coar/resource_type/c_3248
dc.description.reviewstatuspeerReviewed
dc.format.pagerange315-325
dc.relation.issn1871-3033
dc.type.versionacceptedVersion
dc.rights.copyright© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
dc.rights.accesslevelopenAccessfi
dc.subject.ysokoneoppiminen
dc.subject.ysomallintaminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p3533
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1007/978-3-031-29082-4_18
jyx.fundinginformationThis research was supported by the University of Jyväskylä.
dc.type.okmA3


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