@Article{NevesKöFoGiWiCoHe:2020:SeSeBr,
               author = "Neves, Alana Kasahara and K{\"o}rting, Thales Sehn and Fonseca, 
                         Leila Maria Garcia and Girolamo Neto, Cesare Di and Wittich, D. 
                         and Costa, G. A. O. P. and Heipke, C.",
          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 {Leibniz Universit{\"a}t Hannover} and 
                         {Universidade do Estado do Rio de Janeiro (UERJ)} and {Leibniz 
                         Universit{\"a}t Hannover}",
                title = "Semantic segmentation of brazilian savanna vegetation using high 
                         spatial resolution satellite data and u-net",
              journal = "ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial 
                         Information Sciences",
                 year = "2020",
               volume = "5",
               number = "3",
                pages = "505--511",
                month = "Aug.",
                 note = "{Pr{\^e}mio CAPES Elsevier 2023 - ODS 15: Vida terrestre}",
             keywords = "Cerrado, biome, physiognomies, pixel-wise classification, Remote 
                         Sensing, Deep Learning.",
             abstract = "Large-scale mapping of the Brazilian Savanna (Cerrado) vegetation 
                         using remote sensing images is still a challenge due to the high 
                         spatial variability and spectral similarity of the different 
                         characteristic vegetation types (physiognomies). In this paper, we 
                         report on semantic segmentation of the three major groups of 
                         physiognomies in the Cerrado biome (Grasslands, Savannas and 
                         Forests) using a fully convolutional neural network approach. The 
                         study area, which covers a Brazilian conservation unit, was 
                         divided into three regions to enable testing the approach in 
                         regions that were not used in the training phase. A WorldView-2 
                         image was used in cross validation experiments, in which the 
                         average overall accuracy achieved with the pixel-wise 
                         classifications was 87.0%. The F-1 score values obtained with the 
                         approach for the classes Grassland, Savanna and Forest were of 
                         0.81, 0.90 and 0.88, respectively. Visual assessment of the 
                         semantic segmentation outcomes was also performed and confirmed 
                         the quality of the results. It was observed that the confusion 
                         among classes occurs mainly in transition areas, where there are 
                         adjacent physiognomies if a scale of increasing density is 
                         considered, which agrees with previous studies on natural 
                         vegetation mapping for the Cerrado biome.",
                  doi = "10.5194/isprs-Annals-V-3-2020-505-2020",
                  url = "http://dx.doi.org/10.5194/isprs-Annals-V-3-2020-505-2020",
                 issn = "0924-2716",
             language = "en",
           targetfile = "Neves_semantic.pdf",
        urlaccessdate = "28 abr. 2024"
}