@InProceedings{PereiraSant:2019:HoEfSu,
               author = "Pereira, Matheus Barros and Santos, Jefersson Alex dos",
          affiliation = "{Universidade Federal de Minas Gerais} and {Universidade Federal 
                         de Minas Gerais}",
                title = "How effective is super-resolution to improve dense labelling of 
                         coarse resolution imagery?",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "super-resolution, semantic segmentation, remote sensing.",
             abstract = "Coarse resolution remote sensing images, such as LANDSAT and MODIS 
                         are easily found in public open repositories and, therefore, are 
                         widely used in many studies. But their use for automatic creation 
                         of thematic maps is very restrict since most of the deep-based 
                         semantic segmentation (a.k.a dense labelling) approaches are only 
                         suitable for subdecimeter data. In this paper, we design a 
                         straightforward framework in order to evaluate the effectiveness 
                         of deep-based super-resolution in the semantic segmentation of 
                         low-resolution remote sensing images. We carried out an extensive 
                         set of experiments on three remote sensing datasets with distinct 
                         nature/properties. The results show that super-resolution is 
                         effective to improve semantic segmentation performance on 
                         low-resolution aerial imagery. It not only outperforms 
                         unsupervised interpolation but also achieves semantic segmentation 
                         results comparable to high-resolution data.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00035",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00035",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U2HNG8",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U2HNG8",
           targetfile = "45.pdf",
        urlaccessdate = "2024, Apr. 27"
}