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...
| Autores: | , , |
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| 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 |
| 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. ... |
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