@InProceedings{DutraOliReiCalLu:2017:CoAgSy,
               author = "Dutra, Luciano Vieira and Oliveira, Maria Ant{\^o}nia Falc{\~a}o 
                         de and Reis, Mariane Souza and Calvi, Miqu{\'e}ias Freitas and 
                         Lu, Dengsheng",
          affiliation = "{} and {} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Cocoa agroforest systems classification with high resolution 
                         images",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "304--311",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The objective of this work is to verify the viability of cocoa 
                         agroforestry classification with High Resolution imagery to 
                         include, in the cropland area, the mapping of cocoa planted under 
                         forest, as well as open cocoa plantation. In order to avoid 
                         overestimating the cocoa area, we introduce the concept of 
                         counter-examples (CE). Counter-examples are areas of known 
                         classes, not directly involved in classification focus, but 
                         identified to avoid the classes of focus being misleadingly 
                         classified. Two set of CE was used. The first one is the merging 
                         of 7 non-cocoa classes in one training set. The other uses each of 
                         these 7 CE classes separately in the training set. Among the 
                         several classifiers tested, the best one was SVM with RBF kernel. 
                         Results showed that using one CE set produces a more uniform 
                         classification map than using 7 CE separately and captures 20% 
                         more cocoa cultivated area in a test field, than mapping open 
                         cocoa only, with similar user accuracy.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "60283",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PS43PA",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PS43PA",
           targetfile = "60283.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "27 abr. 2024"
}