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dc.contributor.authorTuominen, Sakari
dc.contributor.authorNäsi, Roope
dc.contributor.authorHonkavaara, Eija
dc.contributor.authorBalazs, Andras
dc.contributor.authorHakala, Teemu
dc.contributor.authorViljanen, Niko
dc.contributor.authorPölönen, Ilkka
dc.contributor.authorSaari, Heikki
dc.contributor.authorOjanen, Harri
dc.date.accessioned2018-06-04T06:23:41Z
dc.date.available2018-06-04T06:23:41Z
dc.date.issued2018
dc.identifier.citationTuominen, S., Näsi, R., Honkavaara, E., Balazs, A., Hakala, T., Viljanen, N., Pölönen, I., Saari, H., & Ojanen, H. (2018). Assessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity. <i>Remote Sensing</i>, <i>10</i>(5), Article 714. <a href="https://doi.org/10.3390/rs10050714" target="_blank">https://doi.org/10.3390/rs10050714</a>
dc.identifier.otherCONVID_28076309
dc.identifier.otherTUTKAID_77762
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/58309
dc.description.abstractRecognition of tree species and geospatial information on tree species composition is essential for forest management. In this study, tree species recognition was examined using hyperspectral imagery from visible to near-infrared (VNIR) and short-wave infrared (SWIR) camera sensors in combination with a 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum with a diverse selection of 26 tree species from 14 genera was used as a test area. Aerial hyperspectral imagery and high spatial resolution photogrammetric color imagery were acquired from the test area using unmanned aerial vehicle (UAV) borne sensors. Hyperspectral imagery was processed to calibrated reflectance mosaics and was tested along with the mosaics based on original image digital number values (DN). Two alternative classifiers, a k nearest neighbor method (k-nn), combined with a genetic algorithm and a random forest method, were tested for predicting the tree species and genus, as well as for selecting an optimal set of remote sensing features for this task. The combination of VNIR, SWIR, and 3D features performed better than any of the data sets individually. Furthermore, the calibrated reflectance values performed better compared to uncorrected DN values. These trends were similar with both tested classifiers. Of the classifiers, the k-nn combined with the genetic algorithm provided consistently better results than the random forest algorithm. The best result was thus achieved using calibrated reflectance features from VNIR and SWIR imagery together with 3D point cloud features; the proportion of correctly-classified trees was 0.823 for tree species and 0.869 for tree genus.fi
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherMDPI
dc.relation.ispartofseriesRemote Sensing
dc.rightsCC BY 4.0
dc.subject.otherhyperspectral imagery
dc.subject.othertree species recognition
dc.subject.otherdense point cloud
dc.subject.otherreflectance calibration
dc.subject.otherUAV
dc.subject.otherrandom forest
dc.subject.othergenetic algorithm
dc.titleAssessment of Classifiers and Remote Sensing Features of Hyperspectral Imagery and Stereo-Photogrammetric Point Clouds for Recognition of Tree Species in a Forest Area of High Species Diversity
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201805312962
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/JournalArticle
dc.date.updated2018-05-31T12:15:06Z
dc.description.reviewstatuspeerReviewed
dc.relation.issn2072-4292
dc.relation.numberinseries5
dc.relation.volume10
dc.type.versionpublishedVersion
dc.rights.copyright© the Authors, 2018.
dc.rights.accesslevelopenAccessfi
dc.subject.ysokaukokartoitus
dc.subject.ysoilmakuvakartoitus
dc.subject.ysospektrikuvaus
dc.subject.ysopuusto
dc.subject.ysolajinmääritys
dc.subject.ysokoneoppiminen
dc.subject.ysogeneettiset algoritmit
dc.subject.ysofotogrammetria
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p2521
jyx.subject.urihttp://www.yso.fi/onto/yso/p2520
jyx.subject.urihttp://www.yso.fi/onto/yso/p26364
jyx.subject.urihttp://www.yso.fi/onto/yso/p13847
jyx.subject.urihttp://www.yso.fi/onto/yso/p17523
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p7987
jyx.subject.urihttp://www.yso.fi/onto/yso/p2525
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3390/rs10050714


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