Näytä suppeat kuvailutiedot

dc.contributor.authorRiihiaho, Kimmo A.
dc.contributor.authorRossi, Tuomo
dc.contributor.authorPölönen, Ilkka
dc.contributor.editorJiang, J.
dc.contributor.editorShaker, A.
dc.contributor.editorZhang, H.
dc.date.accessioned2022-08-19T10:39:35Z
dc.date.available2022-08-19T10:39:35Z
dc.date.issued2022
dc.identifier.citationRiihiaho, K. A., Rossi, T., & Pölönen, I. (2022). HyperBlend : Simulating Spectral Reflectance and Transmittance of Leaf Tissue with Blender. In J. Jiang, A. Shaker, & H. Zhang (Eds.), <i>XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission III</i> (V-3-2022, pp. 471-476). Copernicus Publications. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. <a href="https://doi.org/10.5194/isprs-annals-V-3-2022-471-2022" target="_blank">https://doi.org/10.5194/isprs-annals-V-3-2022-471-2022</a>
dc.identifier.otherCONVID_150900468
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/82728
dc.description.abstractRemotely sensing vegetation condition and health hazards requires modeling the connection of plants’ biophysical and biochemical parameters to their spectral response. Even though many models exist already, the field suffers from lack of access to program code. In this study, we will assess the feasibility of open-source 3D-modeling and rendering software Blender in simulating hyperspectral reflectance and transmittance of leaf tissue to serve as a base for a more advanced large-scale simulator. This is the first phase of a larger HyperBlend project, which will provide a fully open-source, canopy scale leaf optical properties model for simulating remotely sensed hyperspectral images. Test results of the current HyperBlend model show good agreement with real-world measurements with root mean squared error around 1‰. The program code is available at https://github.com/silmae/ hyperblend.en
dc.format.extent711
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherCopernicus Publications
dc.relation.ispartofXXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission III
dc.relation.ispartofseriesISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
dc.rightsCC BY 4.0
dc.subject.otherleaf optical properties model
dc.subject.othersimulation
dc.subject.otherray tracing
dc.subject.otherhyperspectral imaging
dc.subject.otherremote sensing
dc.subject.otheropen source
dc.titleHyperBlend : Simulating Spectral Reflectance and Transmittance of Leaf Tissue with Blender
dc.typeconference paper
dc.identifier.urnURN:NBN:fi:jyu-202208194267
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineComputing, Information Technology and Mathematicsfi
dc.contributor.oppiaineTutkintokoulutusfi
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineLaskennallinen tiedefi
dc.contributor.oppiaineComputing, Information Technology and Mathematicsen
dc.contributor.oppiaineDegree Educationen
dc.contributor.oppiaineMathematical Information Technologyen
dc.contributor.oppiaineComputational Scienceen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange471-476
dc.relation.issn2194-9042
dc.relation.volumeV-3-2022
dc.type.versionpublishedVersion
dc.rights.copyright© Author(s) 2022
dc.rights.accesslevelopenAccessfi
dc.type.publicationconferenceObject
dc.relation.conferenceInternational Society for Photogrammetry and Remote Sensing Congress
dc.relation.grantnumber327862
dc.subject.ysokasvillisuus
dc.subject.yso3D-mallinnus
dc.subject.ysokaukokartoitus
dc.subject.ysoreflektanssi
dc.subject.ysosimulointi
dc.subject.ysohyperspektrikuvantaminen
dc.subject.ysoavoin lähdekoodi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p1756
jyx.subject.urihttp://www.yso.fi/onto/yso/p26739
jyx.subject.urihttp://www.yso.fi/onto/yso/p2521
jyx.subject.urihttp://www.yso.fi/onto/yso/p38165
jyx.subject.urihttp://www.yso.fi/onto/yso/p4787
jyx.subject.urihttp://www.yso.fi/onto/yso/p39290
jyx.subject.urihttp://www.yso.fi/onto/yso/p17089
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.5194/isprs-annals-V-3-2022-471-2022
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundinginformationThis study was funded by Academy of Finland (327862).
dc.type.okmA4


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