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dc.contributor.authorNezami, Somayeh
dc.contributor.authorKhoramshahi, Ehsan
dc.contributor.authorNevalainen, Olli
dc.contributor.authorPölönen, Ilkka
dc.contributor.authorHonkavaara, Eija
dc.date.accessioned2020-05-19T08:59:52Z
dc.date.available2020-05-19T08:59:52Z
dc.date.issued2020
dc.identifier.citationNezami, S., Khoramshahi, E., Nevalainen, O., Pölönen, I., & Honkavaara, E. (2020). Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks. <i>Remote Sensing</i>, <i>12</i>(7), Article 1070. <a href="https://doi.org/10.3390/rs12071070" target="_blank">https://doi.org/10.3390/rs12071070</a>
dc.identifier.otherCONVID_35665698
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/69060
dc.description.abstractInterest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.ispartofseriesRemote Sensing
dc.rightsCC BY 4.0
dc.subject.otherdeep learning
dc.subject.otherdrone imagery
dc.subject.otherhyperspectral image classification
dc.subject.othertree species classification
dc.subject.other3D convolutional neural networks
dc.titleTree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202005193313
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.relation.issn2072-4292
dc.relation.numberinseries7
dc.relation.volume12
dc.type.versionpublishedVersion
dc.rights.copyright© 2020 by the authors. Licensee MDPI, Basel, Switzerland
dc.rights.accesslevelopenAccessfi
dc.subject.ysomiehittämättömät ilma-alukset
dc.subject.ysokoneoppiminen
dc.subject.ysoneuroverkot
dc.subject.ysometsänarviointi
dc.subject.ysokaukokartoitus
dc.subject.ysopuulajit
dc.subject.ysospektrikuvaus
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p24149
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p7292
jyx.subject.urihttp://www.yso.fi/onto/yso/p18894
jyx.subject.urihttp://www.yso.fi/onto/yso/p2521
jyx.subject.urihttp://www.yso.fi/onto/yso/p13848
jyx.subject.urihttp://www.yso.fi/onto/yso/p26364
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3390/rs12071070
jyx.fundinginformationThis research was financially supported by the Business Finland DroneKnowledge project (Dnro 973 1617/31/2016) and by the Academy of Finland project “Autonomous tree health analyzer based on imaging UAV spectrometry” (Decision number 327861).


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