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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/397CJD8
Repositorysid.inpe.br/mtc-m19/2011/02.17.17.34   (restricted access)
Last Update2011:10.13.13.14.36 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19/2011/02.17.17.34.20
Metadata Last Update2018:06.05.04.24.22 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1016/j.patrec.2010.02.008
ISSN0167-8655
Citation KeyLeiteFeFoCoPaSa:2011:HiMaMo
TitleHidden Markov Models for crop recognition in remote sensing image sequences
Year2011
MonthJan.
Access Date2024, May 04
Secondary TypePRE PI
Number of Files1
Size687 KiB
2. Context
Author1 Leite, P. B. C
2 Feitosa, R. Q
3 Formaggio, Antônio Roberto
4 Costa, Gilson Alexandre Ostwald Pedro da
5 Pakzad, K.
6 Sanches, Ieda Del’Arco
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JGJQ
Group1
2
3 DSR-OBT-INPE-MCT-BR
4 DSR-OBT-INPE-MCT-BR
Affiliation1
2
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
JournalPattern Recognition Letters
Volume32
Number1
Pages19-2
History (UTC)2011-02-17 17:41:52 :: marciana :: 2010 -> 2011
2011-10-13 13:11:28 :: marciana -> administrator :: 2011
2011-10-13 13:11:30 :: administrator -> marciana :: 2011
2011-10-13 13:14:36 :: marciana -> administrator :: 2011
2018-06-05 04:24:22 :: administrator -> marciana :: 2011
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsCrop recognition
Hidden Markov Models
Remote sensing
AbstractThis work proposes a Hidden Markov Model (HMM) based technique to classify agricultural crops. The method uses HMM to relate the varying spectral response along the crop cycle with plant phenology, for different crop classes, and recognizes different agricultural crops by analyzing their spectral profiles over a sequence of images. The method assigns each image segment to the crop class whose corresponding HMM delivers the highest probability of emitting the observed sequence of spectral values. Experimental analysis was conducted upon a set of 12 co-registered and radiometrically corrected LANDSAT images of region in southeast Brazil, of approximately 124.100 ha, acquired between 2002 and 2004. Reference data was provided by visual classification, validated through extensive field work. The HMM-based method achieved 93% average class accuracy in the identification of the correct crop, being, respectively, 10% and 26% superior to multi-date and single-date alternative approaches applied to the same data set.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Hidden Markov Models...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
Languageen
Target Fileleite.pdf
User Groupadministrator
marciana
Visibilityshown
Archiving Policydenypublisher denyfinaldraft24
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/mtc-m19@80/2009/08.21.17.02.53
Next Higher Units8JMKD3MGPCW/3ER446E
DisseminationWEBSCI; PORTALCAPES; COMPENDEX.
Host Collectionsid.inpe.br/mtc-m19@80/2009/08.21.17.02
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress electronicmailaddress format isbn label lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate secondarymark session shorttitle sponsor subject tertiarymark tertiarytype typeofwork url
7. Description control
e-Mail (login)marciana
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