@Article{LuLiMorDutBat:2014:RoTeIm,
               author = "Lu, D. and Li, G. and Moran, E. and Dutra, Luciano Vieira and 
                         Batistella, M.",
          affiliation = "Zhejiang Provincial Key Laboratory of Carbon Cycling in Forest 
                         Ecosystems and Carbon Sequestration, School of Environmental \& 
                         Resource Sciences, Zhejiang A\&F UniversityHangzhou, Zhejiang 
                         Province, China; Center for Global Change and Earth Observations, 
                         Michigan State UniversityEast Lansing, MI, United States and 
                         Center for Global Change and Earth Observations, Michigan State 
                         UniversityEast Lansing, MI, United States and Center for Global 
                         Change and Earth Observations, Michigan State UniversityEast 
                         Lansing, MI, United States and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and Embrapa Satellite MonitoringCampinas, SP, 
                         Brazil",
                title = "The roles of textural images in improving land-cover 
                         classification in the Brazilian Amazon",
              journal = "International Journal of Remote Sensing",
                 year = "2014",
               volume = "35",
               number = "24",
                pages = "8188--8207",
             keywords = "Maximum likelihood, Pixels, Satellites, Synthetic aperture radar, 
                         Textures, Advanced land observing satellites, Classification 
                         accuracy, Correlation coefficient, Grey-level co-occurrence 
                         matrixes, Land-cover classification, Landsat Thematic Mapper, 
                         Maximum likelihood classifiers, Phased array type l-band synthetic 
                         aperture radars, Image texture.",
             abstract = "Texture 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 
                         land-cover classification. The classification accuracy can be 
                         improved by 5.2-13.4% as the pixel size changes from 30 to 
                         0.6 m.",
                  doi = "10.1080/01431161.2014.980920",
                  url = "http://dx.doi.org/10.1080/01431161.2014.980920",
                 issn = "0143-1161",
                label = "scopus 2015-01 LuLiMorDutBat:2014:RoTeIm",
             language = "en",
        urlaccessdate = "28 abr. 2024"
}