@InProceedings{FerreiraSantPico:2019:EvDiMe,
               author = "Ferreira, Karine Reis and Santos, Lorena Alves dos and Picoli, 
                         Michelle Cristina Ara{\'u}jo",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Evaluating distance measures for image time series clustering in 
                         land use and cover monitoring",
            booktitle = "Proceedings...",
                 year = "2019",
         organization = "MaChine Learning for Earth ObservatioN Workshop (MACLEAN)",
             abstract = "Time series derived from Earth observation satellite images have 
                         been widely used for land use and cover classification and change 
                         detection. Clustering is a common technique performed to discovery 
                         intrinsic patterns on time series data sets, by grouping similar 
                         time series together based on a certain similarity measure. This 
                         short paper describes an ongoing work on evaluating distance 
                         measures for remote sensing image time series clustering using 
                         Self-Organizing Maps (SOM), specifically to land use and cover 
                         monitoring. We present an experiment to evaluate three similarity 
                         measures, Dynamic Time Warping (DTW), Euclidean (ED) and Manhattan 
                         (MD). In this experiment, we show that ED and ED are more accurate 
                         than DTW for remote sensing image time series clustering in land 
                         use and cover application.",
  conference-location = "Wurzburg, Germany",
      conference-year = "20 Sept.",
                 issn = "16130073",
             language = "en",
        urlaccessdate = "27 abr. 2024"
}