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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitemarte.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier3ERPFQRTRW/3A5HU7L
Repositorydpi.inpe.br/marte/2011/07.22.17.27
Last Update2011:07.22.17.27.40 (UTC) tereza@sid.inpe.br
Metadata Repositorydpi.inpe.br/marte/2011/07.22.17.27.40
Metadata Last Update2018:06.06.02.24.39 (UTC) administrator
ISBN978-85-17-00056-0 (Internet)
978-85-17-00057-7 (DVD)
Citation KeyAffonsoGalo:2011:UtReNe
TitleUtilização de Redes Neurais Artificiais na classificação da cobertura da terra na área de influência do reservatório de Porto Colômbia em 1987 e 2009
FormatDVD, Internet.
Year2011
Access Date2024, Dec. 26
Secondary TypePRE CN
Number of Files1
Size223 KiB
2. Context
Author1 Affonso, Juliane Jussara
2 Galo, Maria de Lourdes Bueno Trindade
Affiliation1 Universidade Estadual Paulista – UNESP
2 Universidade Estadual Paulista – UNESP
Author e-Mail Address1 juliane_affonso@hotmail.com
2 mlourdes@hotmail.com
EditorEpiphanio, José Carlos Neves
Galvão, Lênio Soares
e-Mail Addresswanderf@dsr.inpe.br
Conference NameSimpósio Brasileiro de Sensoriamento Remoto, 15 (SBSR).
Conference LocationCuritiba
Date30 abr. - 5 maio 2011
PublisherInstituto Nacional de Pesquisas Espaciais (INPE)
Publisher CitySão José dos Campos
Pages6618-6625
Book TitleAnais
OrganizationInstituto Nacional de Pesquisas Espaciais (INPE)
History (UTC)2011-08-15 15:18:02 :: wanderf@dsr.inpe.br -> tereza@sid.inpe.br :: 2011
2011-09-05 17:29:43 :: tereza@sid.inpe.br -> administrator :: 2011
2018-06-06 02:24:39 :: administrator -> tereza@sid.inpe.br :: 2011
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsartificial neural networks
image classification
land cover mapping
remote sensing
redes neurais artificiais
classificação automática
mapeamento da cobertura da terra
sensoriamento remoto
AbstractIn past few years, infestations of aquatic plants in reservoirs have been studied as an effect of the environmental unbalance caused by pollution and damming of rivers. The excessive amount of aquatic plants and algae in the water is strongly dependent on human occupation in its surroundings, which makes navigation and the production of electricity difficult. To evaluate this condition, the technology of remote sensing could provide a valuable tool in use mapping and land cover in the surroundings in the water. So, this research aimed at verifying the influence of the spatial resolution of multispectral images in the detection and mapping of land cover in the area of the reservoir of Porto Colombia, using multitemporal analysis procedures and supervised classification by artificial neural networks (RNA).In the classification of these images, the input data was constituted by images TM /Landsat, an hypsometric image derived from SRTM data and a texture image derived from TM4/Landsat. Different architectures of neural networks were trained from samples collected at the scene TM/Landsat and network architecture that resulted in the lowest training error was adopted in the individual classification of image. Furthermore, an analysis was made comparing classified images by using cross tabulation, which permits comparing the results obtained in different times. The methodological adopted was adequate to map land cover classes, permitting a effective spectral recognition of the samples.
AreaSRE
TypeUso e Cobertura da Terra
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/3ERPFQRTRW/3A5HU7L
zipped data URLhttp://urlib.net/zip/3ERPFQRTRW/3A5HU7L
Languagept
Target Filep0576.pdf
User Groupwanderf@dsr.inpe.br
tereza@sid.inpe.br
Visibilityshown
5. Allied materials
Mirror Repositoryurlib.net/www/2011/03.29.20.55
Host Collectiondpi.inpe.br/banon/2003/12.10.19.30
6. Notes
Empty Fieldsarchivingpolicy 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 session shorttitle sponsor subject tertiarymark tertiarytype url versiontype volume
7. Description control
e-Mail (login)tereza@sid.inpe.br
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