@InProceedings{Adeu:2019:EvGrSe,
               author = "Adeu, Rodrigo de Sales da Silva",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Evaluating Growing Self-Organizing Maps for Satellite Image Time 
                         Series Clustering",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Santos, R. D. C. and Queiroz, G. R.",
         organization = "Workshop de Computa{\c{c}}{\~a}o Aplicada, 19. (WORCAP)",
             abstract = "Mapping land use and cover changes of Earth is central to 
                         understand the agricultural dynamics on the Brazilian territory. 
                         Following the high availability of Earth observation satellite 
                         images, time series analysis provides new opportunities and 
                         challenges for land use and land cover change analysis. This 
                         approach usually needs a large set of field samples to be used by 
                         the training and clustering algorithms. In this context, analyze 
                         the samples quality is crucial, since it directly influence the 
                         clustering results. This work uses Self-Organizing Maps (SOMs) to 
                         address the clustering of satellite image time series. In order to 
                         simplify the sizing and parameterization of these algorithms, this 
                         work uses Growing Self-Organizing Maps (GSOMs) to evaluate land 
                         use and cover changes samples.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP",
      conference-year = "17-19 set. 2019",
             language = "en",
           targetfile = "Adeu_evaluating.pdf",
        urlaccessdate = "20 maio 2024"
}