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| 9. |  | MARQUES, J. PR.; ASTUA, J. de F.; KITAJIMA, E. W.; APPEZZATO-DA-GLÓRIA, B. Lesões foliares e de ramos de laranjeira-doce causadas pela leprose-dos-citros Pesquisa Agropecuária Brasileira, Brasília, DF, v. 42, n. 11, p. 1531-1536, nov. 2007 Título em inglês: Foliar and twigs lesions of sweet orange caused by citrus leprosis.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 10. | | SUGUINO, E.; APPEZZATO-DA-GLÓRIA, B.; ARAÚJO, P. S. R. de; SIMÃO, S. Propagação vegetativa de camu-camu por meio de enxertia intergenérica na família Myrtaceae. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 38, n. 12, p. 1477-1482, dez. 2003 Notas Científicas.
Título em inglês: Vegetative propagation of camu-camu by intergeneric grafting in Myrtaceae family.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 11. | | CAMARGO, M. S.; COUTINHO, I. D.; LOURENÇO, S. A.; SOARES, M. K. M.; COLNAGO, L. A.; APPEZZATO-DA-GLÓRIA, B.; CAVALHEIRO, A. J.; AMORIM, L. Potential prophylactic role of silicon against brown rust (Puccinia melanocephala) in sugarcane. European Journal of Plant Pathology, v. 157, n. 1, 2020. 77 - 88| Biblioteca(s): Embrapa Instrumentação. |
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Registro Completo
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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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