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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemarte2.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP6W34M/3JM4973
Repositorysid.inpe.br/marte2/2015/06.15.14.56.25
Last Update2015:06.15.14.56.25 (UTC) banon
Metadata Repositorysid.inpe.br/marte2/2015/06.15.14.56.26
Metadata Last Update2018:06.06.03.19.25 (UTC) administrator
ISBN978-85-17-0076-8
Label309
Citation KeyDutraNegrSantLu:2015:CaStNe
TitleDevelopment of dissimilarity functions using stochastic distances for region-based land cover classification: a case study near Tapajós Flona, Pará state, Brazil
FormatInternet
Year2015
Access Date2024, Apr. 27
Secondary TypePRE CN
Number of Files1
Size580 KiB
2. Context
Author1 Dutra, Luciano Vieira
2 Negri, Rogério Galante
3 Sant'Anna, Sidnei João Siqueira
4 Lu, Dengsheng
Resume Identifier1 8JMKD3MGP5W/3C9JHMA
2
3 8JMKD3MGP5W/3C9JJ8N
Group1 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 dutra@dpi.inpe.br
EditorGherardi, Douglas Francisco Marcolino
Aragão, Luiz Eduardo Oliveira e Cruz de
e-Mail Addresswanderf@dsr.inpe.br
Conference NameSimpósio Brasileiro de Sensoriamento Remoto, 17 (SBSR)
Conference LocationJoão Pessoa
Date25-29 abr. 2015
PublisherInstituto Nacional de Pesquisas Espaciais (INPE)
Publisher CitySão José dos Campos
Pages1655-1662
Book TitleAnais
OrganizationInstituto Nacional de Pesquisas Espaciais (INPE)
History (UTC)2015-06-15 14:56:26 :: banon -> administrator ::
2018-06-06 03:19:25 :: administrator -> simone :: 2015
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
AbstractOne recent alternative to standard pixel based classification of remote sensing data, is the region based classification (RBC), which has been proved particularly useful when analyzing high resolution imagery of complex environments, like urban areas. First the imagery is decomposed into homogenous regions, following some criteria, and then each region is classified to one of the classes of interest. Normally, classification is performed by using stochastic distances, which measures the distance of the pixels distribution inside an unknown region and the representative distributions of each class. The class, whose distance is minimum to the unknown region distribution, is assigned to the region, which is known as stochastic minimum distance classification (SMDC). A problem appears when one, or more, class distribution is multi-modal, which violates the Gaussian hypotheses used for classes distributions, degrading the mapping accuracy. This investigation reports the usage of different compositions of the original stochastic minimum distance classifier with the objective of getting less sensitive results for classification, when potentially multi-modal classes are used. The newly developed classifier, called stochastic nearest distance classifier (SNDC), produced the best result when compared with the original classifier and other possible compositions, in a study case near the Tapajós Flona, in Pará state, Brazil. This study also brings, as methodological contribution, a criterion to improve the segmentation phase of RBC methods.
AreaSRE
TypeClassificação e mineração de dados
Arrangement 1urlib.net > BDMCI > Fonds > SBSR > SBSR 17 > Development of dissimilarity...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Development of dissimilarity...
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/8JMKD3MGP6W34M/3JM4973
zipped data URLhttp://urlib.net/zip/8JMKD3MGP6W34M/3JM4973
Languageen
Target Filep0309.pdf
User Groupbanon
Visibilityshown
5. Allied materials
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Next Higher Units8JMKD3MGP6W34M/3JJLLP2
8JMKD3MGPCW/3EQCCU5
Citing Item Listsid.inpe.br/marte2/2015/05.31.21.54 5
sid.inpe.br/mtc-m21/2012/07.13.15.00.20 2
sid.inpe.br/mtc-m21/2012/07.13.14.53.50 1
Host Collectiondpi.inpe.br/marte2/2013/05.17.15.03.06
6. Notes
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