@InProceedings{MatosSáncCarn:2022:DeLeAu,
               author = "Matos, Diego Henrique M. and S{\'a}nchez Ipia, Alber Hamersson 
                         and Carneiro, Tiago G. S.",
          affiliation = "{Universidade Federal de Minas Gerais (UFMG)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de Ouro Preto (UFOP)}",
                title = "Deep learning automated workflow for cloud segmentation in remote 
                         sensing images",
            booktitle = "Anais...",
                 year = "2022",
               editor = "Rosim, Sergio (INPE) and Santos, Leonardo Bacelar Lima (CEMADEN) 
                         and Pereira, Marconi de Arruda (UFSJ)",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 23. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "In this work, we propose an open source and automated workflow for 
                         semantic segmentation of remote sensing images. Even though it can 
                         be used in other sensors, to evaluate this workflow, a case study 
                         has been conducted applying a deep learning algorithm for 
                         segmenting clouds in images from WFI sensor, onboard CBERS-4A 
                         satellite. Since WFI does not have a tailor-made cloud 
                         segmentation algorithm, we customized our workflow based on the 
                         Unet neural network to fulfill this gap. Our results are promising 
                         according to our tests, although some problems were identified, 
                         like false positives over high albedo targets. These problems 
                         suggest improvements that could be tackled in the future.",
  conference-location = "On-line",
      conference-year = "28 a 30 nov. 2022",
                 issn = "2179-4847",
             language = "en",
                  ibi = "8JMKD3MGPDW34P/487M6K5",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34P/487M6K5",
           targetfile = "192-203_Matos_deep.pdf",
        urlaccessdate = "17 maio 2024"
}