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@InProceedings{BendiniFoKöMaSaAr:2016:AsMuAp,
               author = "Bendini, Hugo do Nascimento and Fonseca, Leila Maria Garcia and 
                         K{\"o}rting, Thales Sehn and Marujo, Rennan de Freitas Bezerra 
                         and Sanches, Ieda Del'Arco and Arcanjo, Jeferson de Souza",
          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 {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Assessment of a multi-sensor approach for noise removal on 
                         Landsat-8 OLI time series using CBERS-4 MUX data to improve crop 
                         classification based on phenological features",
            booktitle = "Anais...",
                 year = "2016",
         organization = "Brazilian Symposium on GeoInformatics, 17. (GEOINFO)",
             abstract = "We investigated a method for noise removal on Landsat-8 OLI 
                         timeseries using CBERS-4 MUX data to improve crop classification. 
                         An algorithm was built to look to the nearest MUX image for each 
                         Landsat image, based on user defined time span. The algorithm 
                         checks for cloud contaminated pixels on the Landsat time series 
                         using Fmask and replaces them with CBERS-4 MUX to build the 
                         integrated time series (Landsat-8 OLI+CBERS-4 MUX). Phenological 
                         features were extracted from the time series samples for each 
                         method (EVI and NDVI original time series and multi-sensor time 
                         series, with and without filtering) and subjected to data mining 
                         using Random Forest classification. In general, we observed a 
                         slight increase in the classification accuracy when using the 
                         proposed method. The best result was observed with the EVI 
                         integrated filtered time series (78%), followed by the filtered 
                         Landsat EVI time series (76%).",
  conference-location = "Campos do Jord{\~a}o, SP",
      conference-year = "27-30 nov. 2016",
             language = "en",
           targetfile = "bendini_assessment.pdf",
        urlaccessdate = "01 dez. 2020"
}


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