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Biblioteca(s): |
Embrapa Tabuleiros Costeiros. |
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Data corrente: |
29/12/2023 |
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Data da última atualização: |
29/12/2023 |
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Tipo da produção científica: |
Resumo em Anais de Congresso |
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Autoria: |
SOUZA, J. L.; SILVA, A. V. C. da. |
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Afiliação: |
JULIANA LOPES SOUZA, UFS; ANA VERUSKA CRUZ DA SILVA MUNIZ, CPATC. |
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Título: |
Banco Ativo de Germoplasma de Mangaba da Embrapa Tabuleiros Costeiros: 17 anos de pesquisa. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
In: SEMINÁRIO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA TABULEIROS COSTEIROS, 12., 2023, Aracaju. Anais... Aracaju: Embrapa Tabuleiros Costeiros, 2023. Editores Técnicos: Viviane Talamini, Ana da Silva Ledo. |
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Idioma: |
Português |
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Thesagro: |
Banco de Germoplasma; Mangaba. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 00606nam a2200133 a 4500
001 2160314
005 2023-12-29
008 2023 bl uuuu u00u1 u #d
100 1 $aSOUZA, J. L.
245 $aBanco Ativo de Germoplasma de Mangaba da Embrapa Tabuleiros Costeiros$b17 anos de pesquisa.$h[electronic resource]
260 $aIn: SEMINÁRIO DE INICIAÇÃO CIENTÍFICA DA EMBRAPA TABULEIROS COSTEIROS, 12., 2023, Aracaju. Anais... Aracaju: Embrapa Tabuleiros Costeiros, 2023. Editores Técnicos: Viviane Talamini, Ana da Silva Ledo.$c2023
650 $aBanco de Germoplasma
650 $aMangaba
700 1 $aSILVA, A. V. C. da
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Embrapa Tabuleiros Costeiros (CPATC) |
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Biblioteca(s): |
Embrapa Territorial. |
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Data corrente: |
03/10/2011 |
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Data da última atualização: |
03/05/2019 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 1 |
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Autoria: |
LU, D.; LI, G.; MORAN, E.; DUTRA, L.; BATISTELLA, M. |
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Afiliação: |
DENGSHENG LU, INDIANA UNIVERSITY; GUIYING LI, INDIANA UNIVERSITY; EMILIO MORAN, INDIANA UNIVERSITY; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM. |
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Título: |
A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
GIScience & Remote Sensing, v. 48, n. 3, p. 345-370, 2011. |
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DOI: |
10.2747/1548-1603.48.3.345 |
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Idioma: |
Português |
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Conteúdo: |
Many data fusion methods are available, but it is poorly understood which fusion method is suitable for integrating Landsat Thematic Mapper (TM) and radar data for land cover classification. This research explores the integration of Landsat TM and radar images (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) for land cover classification in a moist tropical region of the Brazilian Amazon. Different data fusion methods?principal component analysis (PCA), wavelet-merging technique (Wavelet), high-pass filter resolution-merging (HPF), and normalized multiplication (NMM)?were explored. Land cover classification was conducted with maximum likelihood classification based on different scenarios. This research indicates that individual radar data yield much poorer land cover classifications than TM data, and PALSAR L-band data perform relatively better than RADARSAT-2 C-band data. Compared to the TM data, the Wavelet multisensor fusion improved overall classification by 3.3%?5.7%, HPF performed similarly, but PCA and NMM reduced overall classification accuracy by 5.1%?6.1% and 7.6% ?12.7%, respectively. Different polarization options, such as HH and HV, work similarly when used in data fusion. This research underscores the importance of selecting a suitable data fusion method that can preserve spectral fidelity while improving spatial resolution. |
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Palavras-Chave: |
Landsat Thematic Mapper; Wavelet multisensor. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/42776/1/Comparison-fusion-methods-GisRS-2011.pdf
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Marc: |
LEADER 01967naa a2200205 a 4500
001 1902113
005 2019-05-03
008 2011 bl uuuu u00u1 u #d
024 7 $a10.2747/1548-1603.48.3.345$2DOI
100 1 $aLU, D.
245 $aA comparison of multisensor integration methods for land cover classification in the Brazilian Amazon.
260 $c2011
520 $aMany data fusion methods are available, but it is poorly understood which fusion method is suitable for integrating Landsat Thematic Mapper (TM) and radar data for land cover classification. This research explores the integration of Landsat TM and radar images (i.e., ALOS PALSAR L-band and RADARSAT-2 C-band) for land cover classification in a moist tropical region of the Brazilian Amazon. Different data fusion methods?principal component analysis (PCA), wavelet-merging technique (Wavelet), high-pass filter resolution-merging (HPF), and normalized multiplication (NMM)?were explored. Land cover classification was conducted with maximum likelihood classification based on different scenarios. This research indicates that individual radar data yield much poorer land cover classifications than TM data, and PALSAR L-band data perform relatively better than RADARSAT-2 C-band data. Compared to the TM data, the Wavelet multisensor fusion improved overall classification by 3.3%?5.7%, HPF performed similarly, but PCA and NMM reduced overall classification accuracy by 5.1%?6.1% and 7.6% ?12.7%, respectively. Different polarization options, such as HH and HV, work similarly when used in data fusion. This research underscores the importance of selecting a suitable data fusion method that can preserve spectral fidelity while improving spatial resolution.
653 $aLandsat Thematic Mapper
653 $aWavelet multisensor
700 1 $aLI, G.
700 1 $aMORAN, E.
700 1 $aDUTRA, L.
700 1 $aBATISTELLA, M.
773 $tGIScience & Remote Sensing$gv. 48, n. 3, p. 345-370, 2011.
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