author = "Rodrigues, Marcos Lima and K{\"o}rting, Thales Sehn and Queiroz, 
                         Gilberto Ribeiro de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Circular Hough Transform and Balanced Random Forest to Detect 
                         Center Pivots",
            booktitle = "Anais...",
                 year = "2020",
               editor = "Carneiro, Tiago Garcia de Senna (UFOP) and Felgueiras, Carlos 
                         Alberto (INPE)",
                pages = "106--115",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 21. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Water management is a field related to the increased mechanization 
                         of agriculture, mainly through center pivot irrigation systems, 
                         therefore it is important to identify and quantify these systems. 
                         Currently, with 6.95 million hectares, Brazil is among the 10 
                         largest countries in irrigation areas in the world. In this study, 
                         a combined Computer Vision and Machine Learning ap- proach is 
                         proposed for the identification of center pivots in remote sensing 
                         im- ages. The methodology is based on Circular Hough Transform 
                         (CHT) to target detection and Balanced Random Forest (BRF) 
                         classifier using vegetation indices NDVI and SAVI generated from 
                         Landsat 8 and CBERS 4 images, being able to detect up to 90.48% of 
                         center pivots mapped by the Brazilian National Water Agency 
  conference-location = "On-line",
      conference-year = "30 nov. a 03 dez. 2020",
                 issn = "2179-4847",
             language = "en",
                  ibi = "8JMKD3MGPDW34P/43PLCP5",
                  url = "http://urlib.net/rep/8JMKD3MGPDW34P/43PLCP5",
           targetfile = "p10.pdf",
                 type = "Geoinforma{\c{c}}{\~a}o",
        urlaccessdate = "04 mar. 2021"