@Article{DinizGamaAdam:2020:EvPoIn,
               author = "Diniz, Juliana Maria Ferreira de Souza and Gama, F{\'a}bio Furlan 
                         and Adami, Marcos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Evaluation of polarimetry and interferometry of Sentinel-1A SAR 
                         data for land use and land cover of the Brazilian Amazon region",
              journal = "Geocarto International",
                 year = "2020",
               volume = "35",
                pages = "e1773544",
             keywords = "Dual-polarimetric, interferometric coherence, machine learning 
                         algorithms, land cover mapping.",
             abstract = "Synthetic aperture radar (SAR) data has been an alternative for 
                         monitoring ground targets, especially in areas with cloud cover. 
                         This study evaluates the potential of Sentinel-1A attributes for 
                         mapping land use and land cover (LULC) in a region of the 
                         Brazilian Amazon, using two different machine learning 
                         classifiers: Random Forest (RF) and Support Vector Machine (SVM). 
                         Different scenarios were used that combined backscattering, 
                         polarimetry, and interferometry to the classification process, 
                         which was divided into two phases to improve the results. The RF 
                         shows superiority over the SVM for almost all scenarios for the 
                         two phases of the mapping. The scenario with all data, presented 
                         the best results with both classifiers. The final maps with RF and 
                         SVM, obtained a global accuracy of 82.7% and 74.5%, respectively. 
                         This study demonstrated the potential of Sentinel-1 to map LULC 
                         classes in the Amazon region using a classification in two 
                         phases.",
                  doi = "10.1080/10106049.2020.1773544",
                  url = "http://dx.doi.org/10.1080/10106049.2020.1773544",
                 issn = "1010-6049",
                label = "lattes: 7484071887086439 3 DinizGamaAdam:2020:EvPoIn",
             language = "en",
           targetfile = "diniz_evaluatin.pdf",
        urlaccessdate = "27 abr. 2024"
}