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@InProceedings{DallaquaFariFaze:2018:AcLeAp,
               author = "Dallaqua, Fernanda B. J. R. and Faria, Fabio A. and Fazenda, 
                         Alvaro L.",
          affiliation = "UNIFESP and UNIFESP and UNIFESP",
                title = "Active Learning Approaches for Deforested Area Classification",
            booktitle = "Proceedings...",
                 year = "2018",
               editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and 
                         Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and 
                         Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez, 
                         Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de 
                         and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa, 
                         Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus, 
                         Klaus de and Scheer, Sergio",
         organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Forest Monitoring, Active Learning, Remote Sensing Imagery.",
             abstract = "The conservation of tropical forests is a social and ecological 
                         relevant subject because of its important role in the global 
                         ecosystem. Forest monitoring is mostly done by extraction and 
                         analysis of remote sensing imagery (RSI) information. In the 
                         literature many works have been successful in remote sensing image 
                         classification through the use of machine learning techniques. 
                         Generally, traditional learning algorithms demand a representative 
                         and huge training set which can be an expensive procedure, 
                         especially in RSI, where the imagery spectrum varies along seasons 
                         and forest coverage. A semi-supervised learning paradigm known as 
                         active learning (AL) is proposed to solve this problem, as it 
                         builds efficient training sets through iterative improvement of 
                         the model performance. In the construction process of training 
                         sets, unlabeled samples are evaluated by a user-defined heuristic, 
                         ranked and then the most relevant samples are labeled by an expert 
                         user. In this work two different AL approaches (Confidence 
                         Heuristics and Committee) are presented to classify remote sensing 
                         imagery. In the experiments, our AL approaches achieve excellent 
                         effectiveness results compared with well-known approaches existing 
                         in the literature for two different datasets.",
  conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
      conference-year = "Oct. 29 - Nov. 1, 2018",
             language = "en",
           targetfile = "sibgrapiID116.pdf",
        urlaccessdate = "2020, Nov. 29"
}


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