Fechar
Metadados

1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemtc-m16c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP8W/3BT2AF2
Repositóriosid.inpe.br/mtc-m18/2012/05.15.13.21
Última Atualização2012:05.15.13.21.48 (UTC) administrator
Repositório de Metadadossid.inpe.br/mtc-m18/2012/05.15.13.21.48
Última Atualização dos Metadados2018:06.04.03.55.36 (UTC) administrator
ISBN978-85-17-00059-1
Chave de CitaçãoNitzeSchuAsch:2012:CoMaLe
TítuloComparison of machine learning algorithms Random Forest, Artificial Neural Network and Support Vector Machine to Maximum Likelihood for supervised crop type classification
FormatoOn-line.
Ano2012
Data de Acesso20 maio 2024
Tipo SecundárioPRE CI
Número de Arquivos1
Tamanho698 KiB
2. Contextualização
Autor1 Nitze, Ingmar
2 Schulthess, Urs
3 Asche, Hartmut
Endereço de e-Mail do Autor1 ingmarnitze@gmail.com
2 uschulthess@4dmaps.de
3 gislab@uni-potsdam.de
EditorFeitosa, Raul Queiroz
Costa, Gilson Alexandre Ostwald Pedro da
Almeida, Cláudia Maria de
Fonseca, Leila Maria Garcia
Kux, Hermann Johann Heinrich
Endereço de e-Mailwanderf@dsr.inpe.br
Nome do EventoInternational Conference on Geographic Object-Based Image Analysis, 4 (GEOBIA).
Localização do EventoRio de Janeiro
DataMay 7-9, 2012
Editora (Publisher)Instituto Nacional de Pesquisas Espaciais (INPE)
Cidade da EditoraSão José dos Campos
Páginas35-40
Título do LivroProceedings
OrganizaçãoInstituto Nacional de Pesquisas Espaciais (INPE)
Histórico (UTC)2012-05-15 13:21:48 :: wanderf@dsr.inpe.br -> administrator ::
2012-05-30 13:42:27 :: administrator -> wanderf@dsr.inpe.br :: 2012
2012-06-01 15:12:42 :: wanderf@dsr.inpe.br -> marciana :: 2012
2012-06-12 14:28:23 :: marciana -> seki@dsr.inpe.br :: 2012
2012-06-13 15:55:29 :: seki@dsr.inpe.br -> marciana :: 2012
2012-06-14 15:03:55 :: marciana -> administrator :: 2012
2018-06-04 03:55:36 :: administrator -> :: 2012
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Palavras-ChaveCrop Classification
Machine Learning Algorithms
Support Vector Machine
RapidEye
ResumoThe classification and recognition of agricultural crop types is an important application of remote sensing. New machine learning algorithms have emerged in the last years, but so far, few studies only have compared their performance and usability. Therefore, we compared three different state-of-the-art machine learning classifiers, namely Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) as well as the traditional classification method Maximum Likelihood (ML) among each other. For this purpose we classified a dataset of more than 500 crop fields located in the Canadian Prairies with a stratified randomized sampling approach. Up to four multi-spectral RapidEye images from the 2009 growing season were used. We compared the mean overall classification accuracies as well as standard deviations. Furthermore, the classification accuracy of single crops was analysed. Support Vector Machine classifiers using radial basis function or polynomial kernels exhibited superior results to ANN and RF in terms of overall accuracy and robustness, while ML exhibited inferior accuracies and higher variability. Grassland exhibited the best results for early-season mono-temporal analysis. With a multi-temporal approach, the highest accuracies were achieved for Rapeseed and Field Peas. Other crops, such as Wheat, Flax and Lentils were also successfully classified. The users and producers accuracies were higher than 85 %.
ÁreaSRE
TipoClassification
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreementnão têm arquivos
4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/8JMKD3MGP8W/3BT2AF2
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGP8W/3BT2AF2
Idiomaen
Arquivo Alvo015.pdf
Grupo de Usuáriosadministrator
wanderf@dsr.inpe.br
Visibilidadeshown
5. Fontes relacionadas
Repositório Espelhourlib.net/www/2011/03.29.20.55
Acervo Hospedeirosid.inpe.br/mtc-m18@80/2008/03.17.15.17
6. Notas
Campos Vaziosaffiliation archivingpolicy archivist callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition group issn label lineage mark nextedition nexthigherunit notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark serieseditor shorttitle sponsor tertiarymark tertiarytype url versiontype volume


Fechar