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dc.contributor.authorNevalainen, Olli
dc.contributor.authorHonkavaara, Eija
dc.contributor.authorTuominen, Sakari
dc.contributor.authorViljanen, Niko
dc.contributor.authorHakala, Teemu
dc.contributor.authorYu, Xiaowei
dc.contributor.authorHyyppä, Juha
dc.contributor.authorSaari, Heikki
dc.contributor.authorPölönen, Ilkka
dc.contributor.authorImai, Nilton N.
dc.contributor.authorTommaselli, Antonio M. G.
dc.date.accessioned2017-02-28T11:19:04Z
dc.date.available2017-02-28T11:19:04Z
dc.date.issued2017
dc.identifier.citationNevalainen, O., Honkavaara, E., Tuominen, S., Viljanen, N., Hakala, T., Yu, X., Hyyppä, J., Saari, H., Pölönen, I., Imai, N. N., & Tommaselli, A. M. G. (2017). Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. <i>Remote Sensing</i>, <i>9</i>(3), Article 185. <a href="https://doi.org/10.3390/rs9030185" target="_blank">https://doi.org/10.3390/rs9030185</a>
dc.identifier.otherCONVID_26571053
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/53150
dc.description.abstractSmall unmanned aerial vehicle (UAV) based remote sensing is a rapidly evolving technology. Novel sensors and methods are entering the market, offering completely new possibilities to carry out remote sensing tasks. Three-dimensional (3D) hyperspectral remote sensing is a novel and powerful technology that has recently become available to small UAVs. This study investigated the performance of UAV-based photogrammetry and hyperspectral imaging in individual tree detection and tree species classification in boreal forests. Eleven test sites with 4151 reference trees representing various tree species and developmental stages were collected in June 2014 using a UAV remote sensing system equipped with a frame format hyperspectral camera and an RGB camera in highly variable weather conditions. Dense point clouds were measured photogrammetrically by automatic image matching using high resolution RGB images with a 5 cm point interval. Spectral features were obtained from the hyperspectral image blocks, the large radiometric variation of which was compensated for by using a novel approach based on radiometric block adjustment with the support of in-flight irradiance observations. Spectral and 3D point cloud features were used in the classification experiment with various classifiers. The best results were obtained with Random Forest and Multilayer Perceptron (MLP) which both gave 95% overall accuracies and an F-score of 0.93. Accuracy of individual tree identification from the photogrammetric point clouds varied between 40% and 95%, depending on the characteristics of the area. Challenges in reference measurements might also have reduced these numbers. Results were promising, indicating that hyperspectral 3D remote sensing was operational from a UAV platform even in very difficult conditions. These novel methods are expected to provide a powerful tool for automating various environmental close-range remote sensing tasks in the very near future.
dc.language.isoeng
dc.publisherMPDI
dc.relation.ispartofseriesRemote Sensing
dc.subject.otherUAV
dc.subject.otherhyperspectral
dc.subject.otherradiometry
dc.subject.otherpoint cloud
dc.subject.otherforest
dc.titleIndividual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201702271530
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.updated2017-02-27T10:15:06Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn2072-4292
dc.relation.numberinseries3
dc.relation.volume9
dc.type.versionpublishedVersion
dc.rights.copyright© 2017 the Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License.
dc.rights.accesslevelopenAccessfi
dc.subject.ysofotogrammetria
dc.subject.ysoluokitus (toiminta)
jyx.subject.urihttp://www.yso.fi/onto/yso/p2525
jyx.subject.urihttp://www.yso.fi/onto/yso/p12668
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3390/rs9030185
dc.type.okmA1


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© 2017 the Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License.
Except where otherwise noted, this item's license is described as © 2017 the Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License.