@InProceedings{PletschKöOlSaVeGaEs:2019:PoUsSe,
               author = "Pletsch, Mikhaela Alo{\'{\i}}sia J{\'e}ssie Santos and 
                         K{\"o}rting, Thales Sehn and Oliveira, Willian Vieira de and 
                         Sanches, Ieda Del'Arco and Vel{\'a}zquez Fernandez, Victor and 
                         Gama, F{\'a}bio Furlan 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)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade de S{\~a}o Paulo (USP)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Potential of using Sentinel-1 data to distinguish targets in 
                         remote sensing images",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Misra, Sanjay and Gervasi, Osvaldo and Murgante, Beniamino and 
                         Stankova, Elena and Korkhov, Vladimir and Torre, Carmelo and 
                         Rocha, Ana Maria A. C. and Taniar, David and Apduhan, Bernady O. 
                         and Tarantino, Eufemia",
                pages = "563--576",
         organization = "International Conference on Computational Science and its 
                         Applications",
            publisher = "Springer",
             keywords = "Radar, Sentinel, Land cover mapping, Satellite imagery, Pattern 
                         analysis.",
             abstract = "Copernicus is the Worlds largest single Earth Observation (EO) 
                         programme, whose satellite constellations are planned to be 
                         launched between 2014 and 2025. Among the constellations, 
                         Sentinel-1 (S-1) is a C-band SAR able to support land cover 
                         mapping. Although optical data are commonly used for land cover 
                         monitoring, the low availability of cloud-free scenes along the 
                         year hinders the mapping process. In such a way, S-1 presents an 
                         important source of data, able of providing all-weather and 
                         day-and-night imagery of EO. In this study, we investigate the 
                         potential of using S-1 data to distinguish targets in Remote 
                         Sensing images in three different Brazilian biomes, Amazon, 
                         Cerrado, and Atlantic Forest. Based on that, we proposed a 
                         methodology to classify SAR images, which was validated 
                         considering a different area from the ones used for sampling 
                         purposes. The results showed that through S1 data, it is possible 
                         to detect mainly water and urban area targets, with overall 
                         accuracy of 0.90, evidencing that our approach is reproducible in 
                         other regions.",
  conference-location = "Saint Petersburg, Russia",
      conference-year = "01-04 July",
                  doi = "10.1007/978-3-030-24305-0_42",
                  url = "http://dx.doi.org/10.1007/978-3-030-24305-0_42",
                 isbn = "978-303024304-3",
                 issn = "03029743",
             language = "en",
           targetfile = "pletsch_Potential.pdf",
        urlaccessdate = "20 maio 2024"
}