@InProceedings{VazOldKörFonSan:2019:CoPeRe,
               author = "Vaz, Daiane Vieira and Oldoni, Lucas Volochen and K{\"o}rting, 
                         Thales Sehn and Fonseca, Leila Maria Garcia and Sanches, Ieda 
                         Del'Arco",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Comparing per-pixel and region-based classification methods using 
                         CBERS-4/MUX images to analyse land cover change caused by the 
                         Mariana disaster",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "1831--1834",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "MUX, CBERS-4, Mariana Disaster, classification.",
             abstract = "In November 5th, 2015, the Fund{\~a}o dams rupture in Mariana, 
                         Minas Gerais, Brazil, dumped millions of cubic meters of tailing 
                         into the river, causing abrupt changes in the land cover (LC). 
                         Remote Sensing (RS) techniques and image analyses allow monitoring 
                         LC changes, that can contribute for decision making. In this paper 
                         we show results of LC change detection caused by the disaster 
                         applying per-pixel and region-based classifiers. For this purpose, 
                         three CBERS-4/MUX images were independently classified to assess 
                         LC in different periods: prior the incident, right after and its 
                         current situation. The per-pixel classification distinguished 
                         rivers from other classes, better than the region-based 
                         classification. In addition, the changes detected in the LC helped 
                         to highlight vegetation areas affected by the incident and also to 
                         evaluate its effects. Furthermore, the analysis was able to 
                         identify regenerated vegetation areas.",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3U6G43L",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3U6G43L",
           targetfile = "97717.pdf",
                 type = "Mudan{\c{c}}a de uso e cobertura da Terra",
        urlaccessdate = "04 maio 2024"
}