Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network

Pölönen, Ilkka; Annala, Leevi; Rahkonen, Samuli; Nevalainen, Olli; Honkavaara, Eija; Tuominen, Sakari; Viljanen, Niko; Hakala, Teemu

doi:10.1109/WHISPERS.2018.8747253

dc.contributor.author	Pölönen, Ilkka
dc.contributor.author	Annala, Leevi
dc.contributor.author	Rahkonen, Samuli
dc.contributor.author	Nevalainen, Olli
dc.contributor.author	Honkavaara, Eija
dc.contributor.author	Tuominen, Sakari
dc.contributor.author	Viljanen, Niko
dc.contributor.author	Hakala, Teemu
dc.date.accessioned	2020-12-23T10:39:18Z
dc.date.available	2020-12-23T10:39:18Z
dc.date.issued	2019
dc.identifier.citation	Pölönen, 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.other	CONVID_31255771
dc.identifier.other	TUTKAID_81835
dc.identifier.uri	https://jyx.jyu.fi/handle/123456789/73412
dc.description.abstract	In 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.mimetype	application/pdf
dc.language.iso	eng
dc.publisher	IEEE
dc.relation.ispartof	WHISPERS 2018 : 9th Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
dc.rights	In Copyright
dc.subject.other	3D
dc.subject.other	convolutional neural network
dc.subject.other	UAV
dc.title	Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network
dc.type	conferenceObject
dc.identifier.urn	URN:NBN:fi:jyu-202012117078
dc.contributor.laitos	Informaatioteknologian tiedekunta	fi
dc.contributor.laitos	Faculty of Information Technology	en
dc.contributor.oppiaine	Tietotekniikka	fi
dc.contributor.oppiaine	Mathematical Information Technology	en
dc.type.uri	http://purl.org/eprint/type/ConferencePaper
dc.date.updated	2020-12-11T10:15:06Z
dc.relation.isbn	978-1-7281-1581-8
dc.type.coar	http://purl.org/coar/resource_type/c_5794
dc.description.reviewstatus	nonPeerReviewed
dc.relation.issn	2158-6276
dc.type.version	acceptedVersion
dc.rights.copyright	© 2018 IEEE
dc.rights.accesslevel	openAccess	fi
dc.relation.conference	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
dc.relation.grantnumber	1711/31/2016
dc.subject.yso	spektrikuvaus
dc.subject.yso	puulajit
dc.subject.yso	3D-mallinnus
dc.subject.yso	hahmontunnistus (tietotekniikka)
dc.subject.yso	neuroverkot
dc.format.content	fulltext
jyx.subject.uri	http://www.yso.fi/onto/yso/p26364
jyx.subject.uri	http://www.yso.fi/onto/yso/p13848
jyx.subject.uri	http://www.yso.fi/onto/yso/p26739
jyx.subject.uri	http://www.yso.fi/onto/yso/p8266
jyx.subject.uri	http://www.yso.fi/onto/yso/p7292
dc.rights.url	http://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi	10.1109/WHISPERS.2018.8747253
dc.relation.funder	TEKES	fi
dc.relation.funder	TEKES	en
jyx.fundingprogram	Elinkeinoelämän kanssa verkottunut tutkimus, TEKES	fi
jyx.fundingprogram	Public research networked with companies, TEKES	en
jyx.fundinginformation	This research has been co-financed by Finnish Funding Agency for Innovation Tekes (grants 2208/31/2013 and 1711/31/2016)
dc.type.okm	B3

Aineistoon kuuluvat tiedostot

Nimi:: polonen2018.pdf
Koko:: 208.1Kb
Tiedostomuoto:: PDF
Kuvaus:: Final Draft

Katso/Avaa

Aineisto kuuluu seuraaviin kokoelmiin

Informaatioteknologian tiedekunta [2297]

Näytä suppeat kuvailutiedot

Ellei muuten mainita, aineiston lisenssi on In Copyright

Näytetään aineistoja, joilla on samankaltainen nimeke tai asiasanat.

Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks

Nezami, Somayeh; Khoramshahi, Ehsan; Nevalainen, Olli; Pölönen, Ilkka; Honkavaara, Eija (MDPI AG, 2020)

Interest 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 ...
Construction of Multi-resolution Multi-organ Shape Model Based on Stacked Autoencoder Neural Network

Chen, Zhonghua; Wang, Hongkai; Cong, Fengyu; Kettunen, Lauri (IEEE, 2022)

The construction of statistical shape models (SSMs) is an important method in the field of medical image segmentation. Most SSMs are constructed by using traditional modeling methods based on principal component analysis ...
Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion

Annala, Leevi; Honkavaara, Eija; Tuominen, Sakari; Pölönen, Ilkka (MDPI AG, 2020)

Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based ...
Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks

Hakala, Taina; Pölönen, Ilkka; Honkavaara, Eija; Näsi, Roope; Hakala, Teemu; Lindfors, Antti (Springer, 2020)

In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. ...
Prediction of major torso organs in low-contrast micro-CT images of mice using a two-stage deeply supervised fully convolutional network

Wang, Hongkai; Han, Ye; Chen, Zhonghua; Hu, Ruxue; Chatziioannou, Arion F.; Zhang, Bin (Institute of Physics, 2019)

Delineation of major torso organs is a key step of mouse micro-CT image analysis. This task is challenging due to low soft tissue contrast and high image noise, therefore anatomical prior knowledge is needed for accurate ...

Tree Species Identification Using 3D Spectral Data and 3D Convolutional Neural Network

Aineistoon kuuluvat tiedostot

Aineisto kuuluu seuraaviin kokoelmiin

Samankaltainen aineisto

Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks ﻿

Construction of Multi-resolution Multi-organ Shape Model Based on Stacked Autoencoder Neural Network ﻿

Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion ﻿

Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks ﻿

Prediction of major torso organs in low-contrast micro-CT images of mice using a two-stage deeply supervised fully convolutional network ﻿

Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks

Construction of Multi-resolution Multi-organ Shape Model Based on Stacked Autoencoder Neural Network

Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion

Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks

Prediction of major torso organs in low-contrast micro-CT images of mice using a two-stage deeply supervised fully convolutional network