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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemarte2.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier3ERPFQRTRW34M/3E7GDLP
Repositorydpi.inpe.br/marte2/2013/05.28.23.31.06
Last Update2013:05.28.23.31.06 (UTC) administrator
Metadata Repositorydpi.inpe.br/marte2/2013/05.28.23.31.07
Metadata Last Update2018:06.06.03.32.39 (UTC) administrator
ISBN978-85-17-00066-9 (Internet)
978-85-17-00065-2 (DVD)
Label485
Citation KeyArabiPizaPinhFern:2013:ClEsDa
TitleClassificação de espectros de dados hiperespectrais pelo método de sequência típica e modelo oculto de Markov
FormatDVD, Internet.
Year2013
Access Date2024, May 11
Secondary TypePRE CN
Number of Files1
Size370 KiB
2. Context
Author1 Arabi, Samir Youssif Wehbi
2 Pizarro, Marco Antonio
3 Pinho, Marcelo da Silva
4 Fernandes, David
Resume Identifier1
2 8JMKD3MGP5W/3C9JHQH
Group1
2 DEA-ETE-INPE-MCTI-GOV-BR
Affiliation1
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 sywa@ifg.edu.br
EditorEpiphanio, José Carlos Neves
Galvão, Lênio Soares
Conference NameSimpósio Brasileiro de Sensoriamento Remoto, 16 (SBSR)
Conference LocationFoz do Iguaçu
Date13-18 abr. 2013
PublisherInstituto Nacional de Pesquisas Espaciais (INPE)
Publisher CitySão José dos Campos
Pages9004-9011
Book TitleAnais
OrganizationInstituto Nacional de Pesquisas Espaciais (INPE)
History (UTC)2013-05-28 23:31:07 :: banon -> administrator ::
2018-06-06 03:32:39 :: administrator -> marcelo.pazos@inpe.br :: 2013
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
AbstractThis work presents a new methodology for hyperspectral image classification based on two concepts. Former is the Typical Sequence determination, derived from the Information Theory, and the last is the Hidden Markov Chains (HMM). The HMM gives the probability of a given mixture belongs to the HMM model of an endmember (EM) and the Typical Sequence determination does the association of a mixture with a given EM. Five EM from an AVIRIS Scene and six mixtures, generated by a linear model, were used to perform a classification test. It was also done a classification with the SAM (Spectral Angle Mapper), the ED (Euclidian Distance) and with the SID (Self Information Divergence). In the test the proposed method produced the best results showing that it can be used as an alternative method for hyperspectral image classification.
AreaSRE
TypeSensoriamento Remoto Hiperespectral
Arrangement 1urlib.net > BDMCI > Fonds > SBSR > SBSR 16 > Classificação de espectros...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > SBSR 16 > Classificação de espectros...
Arrangement 3Projeto Memória 60... > Livros e livros editados > SBSR 16 > Classificação de espectros...
Arrangement 4urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDEA > Classificação de espectros...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/3ERPFQRTRW34M/3E7GDLP
zipped data URLhttp://urlib.net/zip/3ERPFQRTRW34M/3E7GDLP
Languagept
Target Filep0485.pdf
User Groupadministrator
banon
Visibilityshown
5. Allied materials
Mirror Repositoryiconet.com.br/banon/2006/11.26.21.31
Next Higher Units3ERPFQRTRW34M/3E7G88S
8JMKD3MGPCW/3F6GF6B
Citing Item Listsid.inpe.br/mtc-m21/2012/07.13.14.55.18 2
dpi.inpe.br/marte2/2013/05.28.22.25 2
Host Collectiondpi.inpe.br/marte2/2013/05.17.15.03.06
6. Notes
Empty Fieldsarchivingpolicy archivist callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi e-mailaddress edition issn keywords lineage mark nextedition notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission rightsholder schedulinginformation secondarydate secondarykey secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype url versiontype volume
7. Description control
e-Mail (login)marcelo.pazos@inpe.br
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