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dc.contributor.authorPölönen, Ilkka
dc.contributor.authorAnnala, Leevi
dc.contributor.authorRahkonen, Samuli
dc.contributor.authorNevalainen, Olli
dc.contributor.authorHonkavaara, Eija
dc.contributor.authorTuominen, Sakari
dc.contributor.authorViljanen, Niko
dc.contributor.authorHakala, Teemu
dc.date.accessioned2020-12-23T10:39:18Z
dc.date.available2020-12-23T10:39:18Z
dc.date.issued2019
dc.identifier.citationPölönen, 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 <i>WHISPERS 2018 : 9th Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing</i>. IEEE. <a href="https://doi.org/10.1109/WHISPERS.2018.8747253" target="_blank">https://doi.org/10.1109/WHISPERS.2018.8747253</a>
dc.identifier.otherCONVID_31255771
dc.identifier.otherTUTKAID_81835
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/73412
dc.description.abstractIn this study we apply 3D convolutional neural network (CNN) for tree species identification. Study includes the three most common Finnish tree species. Study uses a relatively large high-resolution spectral data set, which contains also a digital surface model for the trees. Data has been gathered using an unmanned aerial vehicle, a framing hyperspectral imager and a regular RGB camera. Achieved classification results are promising by with overall accuracy of 96.2 % for the classification of the validation data set.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofWHISPERS 2018 : 9th Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
dc.rightsIn Copyright
dc.subject.other3D
dc.subject.otherconvolutional neural network
dc.subject.otherUAV
dc.titleTree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network
dc.typeconferenceObject
dc.identifier.urnURN:NBN:fi:jyu-202012117078
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.date.updated2020-12-11T10:15:06Z
dc.relation.isbn978-1-7281-1581-8
dc.description.reviewstatusnonPeerReviewed
dc.relation.issn2158-6276
dc.type.versionacceptedVersion
dc.rights.copyright© 2018 IEEE
dc.rights.accesslevelopenAccessfi
dc.relation.conferenceWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
dc.relation.grantnumber1711/31/2016
dc.subject.ysospektrikuvaus
dc.subject.ysopuulajit
dc.subject.yso3D-mallinnus
dc.subject.ysohahmontunnistus (tietotekniikka)
dc.subject.ysoneuroverkot
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p26364
jyx.subject.urihttp://www.yso.fi/onto/yso/p13848
jyx.subject.urihttp://www.yso.fi/onto/yso/p26739
jyx.subject.urihttp://www.yso.fi/onto/yso/p8266
jyx.subject.urihttp://www.yso.fi/onto/yso/p7292
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1109/WHISPERS.2018.8747253
dc.relation.funderTEKESfi
dc.relation.funderTEKESen
jyx.fundingprogramElinkeinoelämän kanssa verkottunut tutkimus, TEKESfi
jyx.fundingprogramPublic research networked with companies, TEKESen
jyx.fundinginformationThis research has been co-financed by Finnish Funding Agency for Innovation Tekes (grants 2208/31/2013 and 1711/31/2016)


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