@InProceedings{OliveiraCarBueRodMar:2019:ExPaGe,
               author = "Oliveira, S{\'a}vio S. T. de and Cardoso, Marcelo de C. and 
                         Bueno, Elivelton and Rodrigues, Vagner J. S. and Martins, 
                         Wellington S.",
          affiliation = "{Universidade Federal de Goi{\'a}s (UFG)} and {Universidade 
                         Federal de Goi{\'a}s (UFG)} and {Universidade Federal de 
                         Goi{\'a}s (UFG)} and {Universidade Federal de Goi{\'a}s (UFG)} 
                         and {Universidade Federal de Goi{\'a}s (UFG)}",
                title = "Exploiting parallelism to generate meta-features for land use and 
                         land cover classification with remote sensing time series",
            booktitle = "Anais... do 20º Simp{\'o}sio Brasileiro de Geoinform{\'a}tica",
                 year = "2019",
               editor = "Lisboa Filho, Jugurta and Monteiro, Antonio Miguel Vieira",
         organization = "Simp{\'o}sio Brasileiro de Geoinform{\'a}tica, 20. (GEOINFO)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "geoinformatica.",
             abstract = "The automatic classification of remote sensing time series has 
                         become essential to identify the rapid and frequent changes that 
                         the earths surface has been undergoing. This work investigates the 
                         accuracy of land use and land cover classification with remote 
                         sensing time series when distance based metafeatures are added to 
                         existing features of some classifiers. The distance based 
                         meta-features presented are generated by comparing all time series 
                         of the region being studied to every time series patterns 
                         previously calculated for that region. This is a very costly 
                         operation that was made viable through the use of parallel 
                         processing. Although expensive, this operation is advantageous 
                         because the meta-features generated can be later used as input to 
                         any classifier. The experimental work conducted showed promising 
                         results when using the distance based meta-feature strategy. The 
                         proposed strategy was able to increase from 78% to 93,8% the 
                         classification accuracy of the KNN algorithm, and from 92,3% to 
                         93,8% the accuracy of a state-of-art SVM-based algorithm proposed 
                         recently. These results indicate that distance-based meta-features 
                         allow revealing unknown data characteristics, potentially 
                         increasing classification accuracy.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos",
      conference-year = "11 -13 nov. 2019",
                 issn = "2179-4847",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGPDW34R/3UFDG6B",
                  url = "http://urlib.net/ibi/8JMKD3MGPDW34R/3UFDG6B",
           targetfile = "135-146.pdf",
        urlaccessdate = "19 abr. 2024"
}