@InProceedings{ZanottaShim:2015:MeSeDe,
               author = "Zanotta, Daniel Capella and Shimabukuro, Yosio Edemir",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Uma metodologia semi-supervisionada para detec{\c{c}}{\~a}o de 
                         desmatamento na Amaz{\^o}nia",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1415--1422",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "This paper presents an adaptive framework for recursive 
                         deforestation detection in image time series. With the 
                         availability of reference data for only one image pair from the 
                         time series (source domain), the proposed methodology employs 
                         change vector analysis in the 3-dimensional spherical domain to 
                         determine a decision region R associated with the deforestation 
                         change. Then, by exploiting the similarity among domains, 
                         deforestation can be detected by adapting R to the rest of image 
                         pairs belonging to the time series. The methodology was tested in 
                         a multispectral time series made up by TM-Landsat images marked by 
                         sequential deforestation activities in the Amazon with reference 
                         data. The quantitative analysis of the results indicates the 
                         soundness of the proposed approach.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "263",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM484S",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM484S",
           targetfile = "p0263.pdf",
                 type = "Floresta e vegeta{\c{c}}{\~a}o",
        urlaccessdate = "27 abr. 2024"
}