1. Identificação | |
Tipo de Referência | Artigo em Revista Científica (Journal Article) |
Site | mtc-m12.sid.inpe.br |
Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
Identificador | 6qtX3pFwXQZ3r59YD6/JgabV |
Repositório | sid.inpe.br/iris@1912/2005/11.24.17.10 (acesso restrito) |
Última Atualização | 2005:11.24.17.10.00 (UTC) marciana |
Repositório de Metadados | sid.inpe.br/iris@1912/2005/11.24.17.10.53 |
Última Atualização dos Metadados | 2018:06.05.00.40.33 (UTC) administrator |
Chave Secundária | INPE-13202-PRE/8458 |
DOI | 10.1109/TGRS.2005.848692 |
ISSN | 0196-2892 |
Chave de Citação | HaertelShim:2005:SpLiMi |
Título | Spectral linear mixing model in low spatial resolution image data |
Projeto | Sensoriamento Remoto Aplicado à Ecossistemas Terrestres |
Ano | 2005 |
Mês | Nov. |
Data de Acesso | 15 jun. 2024 |
Tipo de Trabalho | journal article |
Tipo Secundário | PRE PI |
Número de Arquivos | 1 |
Tamanho | 1697 KiB |
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2. Contextualização | |
Autor | 1 Haertel, V. F 2 Shimabukuro, Yosio Edemir |
Identificador de Curriculo | 1 2 8JMKD3MGP5W/3C9JJCQ |
Grupo | 1 2 DSR-INPE-MCT-BR |
Afiliação | 1 Center for Remote Sensing, Federal University at Rio Grande do Sul, Porto Alegre, RS, 91501-970, Brazil 2 Instituto Nacional de Pesquisas Espaciais, Divisão de Sensoriamento Remoto (INPE, DSR) |
Revista | IEEE Transactions on Geoscience and Remote Sensing |
Volume | 43 |
Número | 11 |
Páginas | 2555-2562 |
Histórico (UTC) | 2006-03-17 14:44:13 :: sergio -> administrator :: 2006-09-03 21:43:05 :: administrator -> sergio :: 2007-04-23 21:07:06 :: sergio -> marciana :: 2008-04-04 17:27:28 :: marciana -> administrator :: 2008-06-09 19:20:30 :: administrator -> marciana :: 2011-11-03 11:59:02 :: marciana -> administrator :: 2005 2012-07-13 16:13:03 :: administrator -> marciana :: 2005 2013-03-04 13:55:08 :: marciana -> administrator :: 2005 2016-06-04 23:29:52 :: administrator -> marciana :: 2005 2016-09-14 12:35:08 :: marciana -> administrator :: 2005 2018-06-05 00:40:33 :: administrator -> marciana :: 2005 |
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3. Conteúdo e estrutura | |
É a matriz ou uma cópia? | é a matriz |
Estágio do Conteúdo | concluido |
Transferível | 1 |
Tipo do Conteúdo | External Contribution |
Tipo de Versão | publisher |
Palavras-Chave | Landset Enhanced Thematic Mapper Plus Low spatial resolution image data Mixed pixels Spectral reflectance Mathematical models Mixtures Optical resolving power Radiometers Spectrum analyzers Image analysis |
Resumo | Different ways to estimate the spectral reflectance for the component classes in a mixture problem have been proposed in the literature (pure pixels, spectral library, field measurements). One of the most common approaches consists in the use of pure pixels, i.e., pixels that are covered by a single component class. This approach presents the advantage of allowing the extraction of the components' reflectance directly from the image data. This approach, however, is generally not feasible in the case of low spatial resolution image data, due to the large ground area covered by a single pixel. In this paper, a methodology aiming to overcome this limitation is proposed. The proposed approach makes use of the spectral linear mixing model. In the proposed methodology, the components' proportions in image data are estimated using a medium spatial resolution image as auxiliary data. The linear mixing model is then solved for the unknown spectral reflectances. Experiments are presented, using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus, as low and medium spatial resolution image data, respectively, acquired on the same date over the Tapajos study site, Brazilian Amazon. Three component classes or endmembers are present in the scene covered by the experiment, namely vegetation, exposed soil, and shade. The components' spectral reflectance for the Terra MODIS spectral bands were then estimated by applying the proposed methodology. The reliability of these estimates is appraised by analyzing scatter diagrams produced by the Terra MODIS spectral bands and also by comparing the fraction images produced using both image datasets. This methodology appears appropriate for up-scaling information for regional and global studies. |
Área | SRE |
Arranjo | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDSR > Spectral linear mixing... |
Conteúdo da Pasta doc | acessar |
Conteúdo da Pasta source | não têm arquivos |
Conteúdo da Pasta agreement | não têm arquivos |
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4. Condições de acesso e uso | |
Idioma | en |
Arquivo Alvo | spectral linear.pdf |
Grupo de Usuários | administrator marciana sergio |
Grupo de Leitores | administrator marciana |
Visibilidade | shown |
Detentor da Cópia | SID/SCD |
Política de Arquivamento | denypublisher allowfinaldraft |
Permissão de Leitura | deny from all and allow from 150.163 |
Permissão de Atualização | não transferida |
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5. Fontes relacionadas | |
Unidades Imediatamente Superiores | 8JMKD3MGPCW/3ER446E |
Divulgação | WEBSCI; PORTALCAPES; IEEEXplore. |
Acervo Hospedeiro | sid.inpe.br/banon/2001/04.06.10.52 |
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6. Notas | |
Campos Vazios | alternatejournal archivist callnumber copyright creatorhistory descriptionlevel e-mailaddress electronicmailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress rightsholder schedulinginformation secondarydate secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url |
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7. Controle da descrição | |
e-Mail (login) | marciana |
atualizar | |
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