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@InProceedings{CamargoSanMurAlmAlm:2019:CoAsMa,
               author = "Camargo, Fl{\'a}vio Fortes and Sano, Edson Eyji and Mura, 
                         Jos{\'e} Cl{\'a}udio and Almeida, Cl{\'a}udia Maria de and 
                         Almeida, Tati de",
          affiliation = "{Universidade de Bras{\'{\i}}lia (UnB)} and {Universidade de 
                         Bras{\'{\i}}lia (UnB)} 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",
                 year = "2019",
         organization = "International Geoscience and Remote Sensing Symposium (IGARSS)",
                 note = "Publicado na revista: Remote Sensing, v.11, 2019",
             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 CloudePottier target decomposition 
                         components, incoherent polarimetric parameters (biomass indices 
                         and polarization ratios), and HH-, HV-, VH-, and VV-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.",
  conference-location = "Yokohama, Japan",
      conference-year = "28 July - 02 Aug.",
                  doi = "10.3390/rs11131600",
                  url = "http://dx.doi.org/10.3390/rs11131600",
             language = "en",
           targetfile = "remotesensing-11-01600.pdf",
        urlaccessdate = "24 jan. 2021"
}


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