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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21b.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34P/3NNBSMH
Repositóriosid.inpe.br/mtc-m21b/2017/04.17.19.37   (acesso restrito)
Última Atualização2017:04.17.19.37.04 (UTC) administrator
Repositório de Metadadossid.inpe.br/mtc-m21b/2017/04.17.19.37.04
Última Atualização dos Metadados2018:06.04.02.27.23 (UTC) administrator
DOI10.1109/JSTARS.2016.2628325
ISSN1939-1404
2151-1535
Chave de CitaçãoGenovezFreSanBenLor:2017:OiSlDe
TítuloOil Slicks Detection From Polarimetric data using stochastic distances between complex wishart distributions
Ano2017
MêsFeb.
Data de Acesso02 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho6245 KiB
2. Contextualização
Autor1 Genovez, Patrícia Carneiro
2 Freitas, Corina da Costa
3 Sant'Anna, Sidnei João Siqueira
4 Bentz, Cristina Maria
5 Lorenzzetti, João Antônio
Identificador de Curriculo1
2
3 8JMKD3MGP5W/3C9JJ8N
4
5 8JMKD3MGP5W/3C9JHEF
Grupo1 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
2 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
3 DIDPI-CGOBT-INPE-MCTIC-GOV-BR
4
5 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Centro de Pesquisa da Petrobrás (CENPES)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 genovez.oilspill@gmail.com
2 corina@dpi.inpe.br
3 sidnei.santanna@inpe.br
4 cris@petrobras.com.br
5 joao.lorenzzetti@inpe.br
RevistaIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume10
Número2
Páginas463-477
Histórico (UTC)2017-04-17 19:37:04 :: simone -> administrator ::
2017-04-17 19:37:04 :: administrator -> simone :: 2017
2017-04-17 19:37:58 :: simone -> administrator :: 2017
2017-06-30 23:50:07 :: administrator -> simone :: 2017
2017-12-14 17:01:16 :: simone -> administrator :: 2017
2018-06-04 02:27:23 :: administrator -> simone :: 2017
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-ChaveInformation theory
oil slicks detection
polarimetry
region-based classification
stochastic distances
synthetic aperture radar (SAR)
uncertainty maps
ResumoPolarimetric synthetic aperture radars (PolSAR) have been used to detect oil slicks at the sea surface. Different techniques to extract information from polarimetric data, using an adequate statistical distribution are currently available. A region-based classifier for PolSAR data - named PolClass - uses a supervised approach to compare stochastic distances between scaled complex Wishart distributions and hypothesis tests to associate confidence levels into the classification results. In this paper, the integrated use of these distances together with the uncertainty maps is applied for the first time to detect oil slicks. A quad-pol Radarsat-2 data, acquired during one open-water controlled exercise, was used to perform this test. The PolClass achieved similar overall accuracies for four stochastic distances, reaching 96.54% of global accuracy, the best result obtained by the Hellinger distance. A comparison between the full-and dual-pol matrices indicated that the results obtained with the VV-HH-HV, HH-HV, and VV-HV polarizations are statistically equivalent, but different from that obtained using the HH-VV. Therefore, the exclusion of the HV channel affected the detection of only mineral oils. The classifier demonstrated the potential to detect the three types of oils released, being more effective in detecting biogenic oils rather than mineral oils. The uncertainty levels increase from the center to the border of the mineral oil slicks, indicating the presence of transition regions, possibly related to different weathering mechanisms. The proposed approach will contribute to the understanding of where different physical and chemical processes may be acting, associating confidence levels to the classification results.
ÁreaSRE
Arranjo 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Oil Slicks Detection...
Arranjo 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Oil Slicks Detection...
Arranjo 3urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Oil Slicks Detection...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 17/04/2017 16:37 1.0 KiB 
4. Condições de acesso e uso
Idiomaen
Arquivo Alvogenovez_oil.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentodenypublisher allowfinaldraft
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3F3NU5S
Lista de Itens Citandosid.inpe.br/mtc-m21/2012/07.13.15.00.20 2
sid.inpe.br/bibdigital/2013/10.18.22.34 1
sid.inpe.br/bibdigital/2013/09.09.15.05 1
DivulgaçãoWEBSCI; IEEEXplore.
Acervo Hospedeirosid.inpe.br/mtc-m21b/2013/09.26.14.25.20
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
e-Mail (login)simone
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