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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3D53LNG
Repositorydpi.inpe.br/plutao/2012/11.28.16.49.08
Last Update2015:03.16.19.30.22 (UTC) administrator
Metadata Repositorydpi.inpe.br/plutao/2012/11.28.16.49.09
Metadata Last Update2018:06.05.00.02.06 (UTC) administrator
Secondary KeyINPE--PRE/
ISBN16113349
13: 9783642332746
ISSN03029743
Labellattes: 8201805132981288 1 NegriDutrSant:2012:StApMi
Citation KeyNegriDutrSant:2012:StApMi
TitleStochastic Approaches of Minimum Distance Method for Region Based Classification
FormatPapel
Year2012
Access Date2024, May 19
Secondary TypePRE CI
Number of Files1
Size2090 KiB
2. Context
Author1 Negri, Rogério Galante
2 Dutra, Luciano Vieira
3 Sant'Anna, Sidinei João Siqueira
Resume Identifier1
2 8JMKD3MGP5W/3C9JHMA
Group1 DPI-OBT-INPE-MCTI-GOV-BR
2 DPI-OBT-INPE-MCTI-GOV-BR
3 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 rogerio@dpi.inpe.br
2 dutra@dpi.inpe.br
3 sidnei@dpi.inpe.br
Editoral, Alvarez et
e-Mail Addressrogerio@dpi.inpe.br
Conference NameProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications;Iberoamerican Congress, 17 (CIARP).
Conference LocationBuenos Aires Berlin
Date2012
PublisherSpringer-Verlag
Volume7441
Pages797-804
Book TitleProceedings
Tertiary TypePaper
History (UTC)2012-11-28 23:06:29 :: lattes -> marciana :: 2012
2013-01-18 16:05:29 :: marciana -> administrator :: 2012
2018-06-05 00:02:06 :: administrator -> :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsComputer vision
Image analysis
Image reconstruction
Remote sensing
Stochastic systems
Classification process
Image simulations
Minimum average distance
Minimum distance
Region-based
Simple approach
Simulation studies
Stochastic approach
stochastic distances
AbstractNormally remote sensing image classification is performed pixelwise which produces a noisy classification. One way of improving such results is dividing the classification process in two steps. First, uniform regions by some criterion are detected and afterwards each unlabeled region is assigned to class of the "nearest" class using a so-called stochastic distance. The statistics are estimated by taking in account all the reference pixels. Three variations are investigated. The first variation is to assign to the unlabeled region a class that has the minimum average distance between this region and each one of reference samples of that class. The second is to assign the class of the closest reference sample. The third is to assign the most frequent class of the k closest reference regions. A simulation study is done to assess the performances. The simulations suggested that the most robust and simple approach is the second variation.
AreaSRE
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/J8LNKAN8RW/3D53LNG
zipped data URLhttp://urlib.net/zip/J8LNKAN8RW/3D53LNG
Languageen
Target Filenegri_stochastic.pdf
User Grouplattes
marciana
Reader Groupadministrator
marciana
Visibilityshown
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
URL (untrusted data)http://www.springerlink.com/content/kuv75681m5806613/
DisseminationCOMPENDEX
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
NotesLecture Notes in Computer Science
Volume 7441 2012
Empty Fieldsarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel doi edition lineage mark mirrorrepository nextedition numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project publisheraddress rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark type


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