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dc.contributor.authorLU, D.pt_BR
dc.contributor.authorLI, G.pt_BR
dc.contributor.authorMORAN, E.pt_BR
dc.contributor.authorDUTRA, L.pt_BR
dc.contributor.authorBATISTELLA, M.pt_BR
dc.date.accessioned2014-12-05T11:11:11Zpt_BR
dc.date.available2014-12-05T11:11:11Zpt_BR
dc.date.created2014-12-05pt_BR
dc.date.issued2014pt_BR
dc.identifier.citationInternational Journal of Remote Sensing, v. 35, n. 24, p. 8188-8207, 2014.pt_BR
dc.identifier.isbn0143-1161pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1001846pt_BR
dc.descriptionTexture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l?Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving landcover classification. The classification accuracy can be improved by 5.2?13.4% as the pixel size changes from 30 to 0.6 m.pt_BR
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectAdvanced Land Observing Satellitept_BR
dc.subjectLand-cover classificationpt_BR
dc.subjectLandsat Thematic Mapperpt_BR
dc.subjectPhased Array type L-band Synthetic Aperture Radarpt_BR
dc.titleThe roles of textural images in improving land-cover classification in the Brazilian Amazon.pt_BR
dc.typeArtigo de periódicopt_BR
dc.date.updated2014-12-09T11:11:11Zpt_BR
riaa.ainfo.id1001846pt_BR
riaa.ainfo.lastupdate2014-12-09pt_BR
dc.identifier.doi10.1080/01431161.2014.980920pt_BR
dc.contributor.institutionDENGSHENG LU, Zhejiang A&F University/Michigan State University; GUIYING LI, Michigan State University; EMILIO MORAN, Michigan State University; LUCIANO DUTRA, INPE; MATEUS BATISTELLA, CNPM.pt_BR
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