@InProceedings{ReisDutrEsca:2017:SiMuMu,
               author = "Reis, Mariane Souza and Dutra, Luciano Vieira and Escada, Maria 
                         Isabel Sobral",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Simultaneous multi-source and multi-temporal land cover 
                         classification using a Compound Maximum Likelihood classifier",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Davis Jr., Clodoveu A. (UFMG) and Queiroz, Gilberto R. de (INPE)",
                pages = "74--85",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 18. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The most widely used change detection method is to classify remote 
                         sensing images independently for each date, and stack them to form 
                         a class sequence vector. However, impossible transitions within 
                         the sequences might occur and errors might be accumulated. To 
                         solve this, we propose a novel al- gorithm called Compound Maximum 
                         Likelihood (CML), based on the Maximum Likelihood classifier (ML). 
                         In CML information from all images is used jointly by considering 
                         the a priori probability of each class sequence. The algorithm was 
                         tested for Synthetic Aperture Radar and optical images 
                         classification in a study area in Para \́ state, within the 
                         Brazilian Amazon. CML presented either similar or very improved 
                         accuracy index values over ML land cover classifica- tions.",
  conference-location = "Salvador",
      conference-year = "04-06 dez. 2017",
                 issn = "2179-4820",
             language = "pt",
                  ibi = "8JMKD3MGPDW34P/3Q5DLCB",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34P/3Q5DLCB",
           targetfile = "8reis_escada.pdf",
        urlaccessdate = "27 abr. 2024"
}