@InProceedings{ZanottaZaniShim:2013:AuDeBu,
               author = "Zanotta, Daniel Capella and Zani, Hiran and Shimabukuro, Yosio 
                         Edemir",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Automatic detection of burned areas in wetlands by remote sensing 
                         multitemporal images",
            booktitle = "Proceedings...",
                 year = "2013",
                pages = "1959--1962",
         organization = "IEEE International Geoscience and Remote Sensing Symposium, 
                         (IGARSS).",
            publisher = "IEEE",
             keywords = "wetlands, Bayes methods, covariance matrices, erosion, 
                         expectation-maximisation algorithm, floods, geophysical image 
                         processing, image classification, statistical analysis, terrain 
                         mapping.",
             abstract = "In this paper, a methodology for automatic detection of burned 
                         areas is suggested. The classification criterion is performed 
                         using Bayesian statistical parameter (mean and covariance matrix) 
                         extracted automatically using the Expectation Maximization 
                         algorithm and taking into account the spectral similarity between 
                         burned and flooded areas. In this work the final process involves 
                         the application of morphological operators of erosion and dilation 
                         of images in order to insert information from the spatial context, 
                         refining the final map. Experiments were conducted to a TM-Landsat 
                         scene with areas affected by fires and seasonal flooding. The 
                         results show that the accuracy is increased with the consideration 
                         of flooding mask and the subsequent application of spatial 
                         context, reaching values up to 97% of accuracy when compared with 
                         a reference map.",
  conference-location = "Melbourne, Australia",
      conference-year = "2013",
                  doi = "10.1109/IGARSS.2013.6723191",
                  url = "http://dx.doi.org/10.1109/IGARSS.2013.6723191",
                 isbn = "978-1-4799-1114-1",
                label = "lattes: 1913003589198061 3 ZanottaZaniShim:2013:AuDeBu",
             language = "en",
               volume = "1",
        urlaccessdate = "16 jun. 2024"
}