@InProceedings{PrudenteViMoOlSaAd:2019:UtDaSA,
               author = "Prudente, Victor Hugo Rohden and Vieira, Denis Corte and 
                         Montibeller, Bruno and Oldoni, Lucas Volochen and Sanches, Ieda 
                         Del'Arco and Adami, Marcos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {University of Tartu} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Utiliza{\c{c}}{\~a}o de dados SAR na classifica{\c{c}}{\~a}o 
                         de esp{\'e}cies agr{\'{\i}}colas de primeira e segunda safra",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "1643--1646",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Random Forest, microondas, banda C, agricultura, Random Forest, 
                         microwave, C-band, agriculture.",
             abstract = "Dados dos sensores SAR possuem a vantagem de serem menos 
                         influenciados por presen{\c{c}}a de nuvens, possibilitando maior 
                         frequ{\^e}ncia temporal para monitoramento de alvos 
                         agr{\'{\i}}colas. Diante disso, o objetivo deste trabalho foi 
                         utilizar dados SAR para discriminar diferentes esp{\'e}cies 
                         agr{\'{\i}}colas e alvos naturais no munic{\'{\i}}pio de Luiz 
                         Eduardo Magalh{\~a}es Bahia, em duas safras. Foi utilizada 
                         abordagem temporal, com 12 imagens SAR/Sentinel-1tanto para a 
                         segunda safra de 2017 quanto para a primeira safra de 2018. O 
                         classificar utilizado foi o Random Forest e as amostras de 
                         treinamento foram adquiridas em visitas de campo. Entre os 
                         resultados, a classifica{\c{c}}{\~a}o do algod{\~a}o obteve as 
                         melhores acur{\'a}cias para ambas as safras. Na segunda safra de 
                         2017 houve confus{\~a}o entre as classes de milheto, milho e 
                         sorgo e entre as classes de eucalipto, caf{\'e} e cerrado, 
                         al{\'e}m das classes grama e pastagem. Para a primeira safra de 
                         2018 obteve-se acur{\'a}cias melhores para separa{\c{c}}{\~a}o 
                         das esp{\'e}cies agr{\'{\i}}colas. ABSTRACT: SAR sensors data 
                         have the advantage of being less influenced by the presence of 
                         clouds, thus allowing higher temporal frequency for the monitoring 
                         of agricultural targets. Therefore, the objective of this work was 
                         to use SAR data to classify different agricultural species and 
                         natural targets in the municipality of Luiz Eduardo Magalh{\~a}es 
                         Bahia, in two harvests. Temporal approach was used, with 12 SAR / 
                         Sentinel-1 images for the second harvest of 2017 and for the first 
                         harvest of 2018. The Random Forest algorithm was used, and the 
                         training samples were acquired at field visits. The classification 
                         of Cotton obtained the best results for both harvests. In the 
                         second crop of 2017 there was confusion among the classes of 
                         millet, corn and sorghum and among the classes of eucalyptus, 
                         coffee and cerrado, besides the grass and pasture classes. Better 
                         accuracy for the separation of agricultural species was obtained 
                         for the first harvest of 2018.",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3U9T4L5",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3U9T4L5",
           targetfile = "97850.pdf",
                 type = "Produ{\c{c}}{\~a}o e previs{\~a}o agr{\'{\i}}cola",
        urlaccessdate = "27 abr. 2024"
}