Land-cover classification in the Brazilian Amazon with the integration of Landsat ETM + and Radarsat data.

Land-cover classification with remotely sensed data in moist tropical regions in a challenge due to the complex biophysical conditions. This paper explores techniques to improve land-cover classification accuracy through a comparative analysis of different combinations of spectral signatures and tex...

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Detalhes bibliográficos
Autores: LU, D., BATISTELLA, M., MORAN, E.
Tipo de documento: artigo
Estado:Versão publicada
Data de publicação:2007
País:Brasil
Recursos:Empresa Brasileira de Pesquisa Agropecuária (Embrapa)
Repositório:Repositório Institucional da EMBRAPA (Repository Open Access to Scientific Information from EMBRAPA - Alice)
Idioma:inglês
OAI Identifier:oai:www.alice.cnptia.embrapa.br:doc/17678
Acesso em linha:http://www.alice.cnptia.embrapa.br/alice/handle/doc/17678
Access Level:Acceso aberto
Palavra-chave:Amazon
Landsat ETM+
land-cover
Descrição
Resumo:Land-cover classification with remotely sensed data in moist tropical regions in a challenge due to the complex biophysical conditions. This paper explores techniques to improve land-cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM +) and Radarsat data. A wavelet-merging technique was used to integrate Landsat ETM + multispectral and panchromatic data or Radarsat data. Grey-level co-occurrence matrix (GLCM) textures based on Landsat ETM + panchromatic of Radarsat data and different sizes of moving windows were examined. A maximum-likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land-cover classification accuracies in Amazonian environments. ...