Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/902113
Unidade da Embrapa/Coleção:: Embrapa Monitoramento por Satélite - Artigo em periódico indexado (ALICE)
Data do documento: 3-Out-2011
Tipo do Material: Artigo em periódico indexado (ALICE)
Autoria: LU, D.
LI, G.
MORAN, E.
DUTRA, L.
BATISTELLA, M.
Informaçães Adicionais: DENGSHENG LU, INDIANA UNIVERSITY; GUIYING LI, INDIANA UNIVERSITY; EMILIO MORAN, INDIANA UNIVERSITY; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM.
Título: A comparison of multisensor integration methods for land cover classification in the Brazilian Amazon.
Edição: 2011
Fonte/Imprenta: GIScience & Remote Sensing, v. 48, n. 3, p. 345-370, 2011.
Idioma: pt_BR
Palavras-chave: Landsat Thematic Mapper
Wavelet multisensor
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.
Ano de Publicação: 2011
Aparece nas coleções:Artigo em periódico indexado (CNPM)

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