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dc.contributor.authorLU, D.pt_BR
dc.contributor.authorBATISTELLA, M.pt_BR
dc.contributor.authorMORAN, E.pt_BR
dc.date.accessioned2014-08-23T06:53:48Z-
dc.date.available2014-08-23T06:53:48Z-
dc.date.created2008-04-22pt_BR
dc.date.issued2007pt_BR
dc.identifier.citationInternational Journal of Remote Sensing, v. 28, n. 24, p. 5447-5459, 2007.pt_BR
dc.identifier.isbn1366-5901pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/17678pt_BR
dc.descriptionLand-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. ...pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectAmazonpt_BR
dc.subjectLandsat ETM+pt_BR
dc.subjectland-coverpt_BR
dc.titleLand-cover classification in the Brazilian Amazon with the integration of Landsat ETM + and Radarsat data.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2014-08-23T06:53:48Zpt_BR
riaa.ainfo.id17678pt_BR
riaa.ainfo.lastupdate2014-08-22pt_BR
dc.identifier.doi16.1080f01431160701227596pt_BR
dc.contributor.institutionD. Lu ( Indiana University); Batistella, M. (Embrapa Monitoramento por Satélite); E. Moran ( Indiana University Bloomington Indiana USA).pt_BR
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