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Metadados

1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/428SFBS
Repositóriosid.inpe.br/mtc-m21c/2020/04.01.13.16   (acesso restrito)
Última Atualização2020:04.01.13.16.20 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21c/2020/04.01.13.16.20
Última Atualização dos Metadados2022:01.04.01.35.03 (UTC) administrator
DOI10.1080/15481603.2020.1712102
ISSN1548-1603
Chave de CitaçãoSotheASLCFDLLMT:2020:CoPeCo
TítuloComparative performance of convolutional neural network, weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data
Ano2020
Mêsapr.
Data de Acesso05 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho3591 KiB
2. Contextualização
Autor 1 Sothe, Camile
 2 Almeida, Cláudia Maria de
 3 Schimalski, M. B.
 4 La Rosa, L. E. C.
 5 Castro, J. D. B.
 6 Feitosa, R. Q.
 7 Dalponte, M.
 8 Lima, C. L.
 9 Liesenberg, V.
10 Miyosh, G. T.
11 Tommaselli, A. M. G.
Identificador de Curriculo 1
 2 8JMKD3MGP5W/3C9JGS3
Grupo 1 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
 2 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Afiliação 1 Instituto Nacional de Pesquisas Espaciais (INPE)
 2 Instituto Nacional de Pesquisas Espaciais (INPE)
 3 Santa Catarina State University (UDESC)
 4 Pontifical Catholic University of Rio de Janeiro (PUC)
 5 Pontifical Catholic University of Rio de Janeiro (PUC)
 6 Pontifical Catholic University of Rio de Janeiro (PUC)
 7 Research and Innovation Centre
 8 Santa Catarina State University (UDESC)
 9 Santa Catarina State University (UDESC)
10 São Paulo State University (UNESP)
11 São Paulo State University (UNESP)
Endereço de e-Mail do Autor 1 camilesothe@yahoo.com.br
 2 claudia.almeida@inpe.br
RevistaGIScience and Remote Sensing
Volume57
Número3
Páginas369-394
Nota SecundáriaB1_GEOCIÊNCIAS B1_CIÊNCIAS_AGRÁRIAS_I B2_INTERDISCIPLINAR B3_CIÊNCIAS_AMBIENTAIS
Histórico (UTC)2020-04-01 13:16:20 :: simone -> administrator ::
2020-04-01 13:16:20 :: administrator -> simone :: 2020
2020-04-01 13:18:43 :: simone -> administrator :: 2020
2022-01-04 01:35:03 :: administrator -> simone :: 2020
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveTropical diversity
individual tree crown
deep learning
imbalanced sample set
unmanned aerial vehicle
ResumoThe classification of tree species can significantly benefit from high spatial and spectral information acquired by unmanned aerial vehicles (UAVs) associated with advanced classification methods. This study investigated the following topics concerning the classification of 16 tree species in two subtropical forest fragments of Southern Brazil: i) the potential integration of UAV-borne hyperspectral images with 3D information derived from their photogrammetric point cloud (PPC); ii) the performance of two machine learning methods (support vector machine - SVM and random forest - RF) when employing different datasets at a pixel and individual tree crown (ITC) levels; iii) the potential of two methods for dealing with the imbalanced sample set problem: a new weighted SVM (wSVM) approach, which attributes different weights to each sample and class, and a deep learning classifier (convolutional neural network - CNN), associated with a previous step to balance the sample set; and finally, iv) the potential of this last classifier for tree species classification as compared to the above mentioned machine learning methods. Results showed that the inclusion of the PPC features to the hyperspectral data provided a great accuracy increase in tree species classification results when conventional machine learning methods were applied, between 13 and 17% depending on the classifier and the study area characteristics. When using the PPC features and the canopy height model (CHM), associated with the majority vote (MV) rule, the SVM, wSVM and RF classifiers reached accuracies similar to the CNN, which outperformed these classifiers for both areas when considering the pixel-based classifications (overall accuracy of 84.4% in Area 1, and 74.95% in Area 2). The CNN was between 22% and 26% more accurate than the SVM and RF when only the hyperspectral bands were employed. The wSVM provided a slight increase in accuracy not only for some lesser represented classes, but also some major classes in Area 2. While conventional machine learning methods are faster, they demonstrated to be less stable to changes in datasets, depending on prior segmentation and hand-engineered features to reach similar accuracies to those attained by the CNN. To date, CNNs have been barely explored for the classification of tree species, and CNN-based classifications in the literature have not dealt with hyperspectral data specifically focusing on tropical environments. This paper thus presents innovative strategies for classifying tree species in subtropical forest areas at a refined legend level, integrating UAV-borne 2D hyperspectral and 3D photogrammetric data and relying on both deep and conventional machine learning approaches.
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4. Condições de acesso e uso
Idiomaen
Arquivo Alvosothe_comparative.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentodenypublisher denyfinaldraft
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3F3NU5S
Lista de Itens Citandosid.inpe.br/mtc-m21/2012/07.13.14.43.49 4
sid.inpe.br/bibdigital/2013/10.18.22.34 3
sid.inpe.br/bibdigital/2013/09.13.21.11 3
DivulgaçãoWEBSCI
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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