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@Article{CamargoSanAlmMurAlm:2019:CoAsMa,
               author = "Camargo, Fl{\'a}vio F. and Sano, Edson E. and Almeida, 
                         Cl{\'a}udia Maria de and Mura, Jos{\'e} Cl{\'a}udio and 
                         Almeida, Tati",
          affiliation = "{Universidade de Bras{\'{\i}}lia (UnB)} and {Embrapa Cerrados} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade de Bras{\'{\i}}lia (UnB)}",
                title = "A comparative assessment of machine-learning techniques for land 
                         use and land cover classification of the Brazilian tropical 
                         savanna using ALOS-2/PALSAR-2 polarimetric images",
              journal = "Remote Sensing",
                 year = "2019",
               volume = "11",
               number = "13",
                pages = "e1600",
                month = "July",
             keywords = "SAR, polarimetry, data mining, thematic mapping, Cerrado.",
             abstract = "This study proposes a workflow for land use and land cover (LULC) 
                         classification of Advanced Land Observing Satellite-2 (ALOS-2) 
                         Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) 
                         images of the Brazilian tropical savanna (Cerrado) biome. The 
                         following LULC classes were considered: forestlands; shrublands; 
                         grasslands; reforestations; croplands; pasturelands; bare 
                         soils/straws; urban areas; and water reservoirs. The proposed 
                         approach combines polarimetric attributes, image segmentation, and 
                         machine-learning procedures. A set of 125 attributes was generated 
                         using polarimetric ALOS-2/PALSAR-2 images, including the van Zyl, 
                         Freeman- Durden, Yamaguchi, and Cloude-Pottier target 
                         decomposition components, incoherent polarimetric parameters 
                         (biomass indices and polarization ratios), and HH-, HV-, VH-, 
                         andVV-polarized amplitude images. These attributes were classified 
                         using the Naive Bayes (NB), DT J48 (DT = decision tree), Random 
                         Forest (RF), Multilayer Perceptron (MLP), and Support Vector 
                         Machine (SVM) algorithms. The RF, MLP, and SVM classifiers 
                         presented the most accurate performances. NB and DT J48 
                         classifiers showed a lower performance in relation to the RF, MLP, 
                         and SVM. The DT J48 classifier was the most suitable algorithm for 
                         discriminating urban areas and natural vegetation cover. The 
                         proposed workflow can be replicated for other SAR images with 
                         different acquisition modes or for other types of vegetation 
                         domains.",
                  doi = "10.3390/rs11131600",
                  url = "http://dx.doi.org/10.3390/rs11131600",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-11-01600.pdf",
        urlaccessdate = "18 jan. 2021"
}


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