@InProceedings{AdeuFerrAndrSant:2020:AsSaIm,
               author = "Adeu, Rodrigo de Sales da Silva and Ferreira, Karine Reis and 
                         Andrade Neto, Pedro Ribeiro de and Santos, Lorena Alves dos",
          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)}",
                title = "Assessing satellite image time series clustering using growing 
                         SOM",
            booktitle = "Proceedings...",
                 year = "2020",
               editor = "Gervasi, O. and Murgante, B. and Misra, S. and Garau, C. and 
                         Blecic, I. and Taniar, D. and Apduhan, B. O. and Rocha, A. M. A. 
                         C. and Tarantino, E. and Torre, C. M. and Karaca, Y.",
                pages = "270--282",
         organization = "International Conference on Computational Science and Its 
                         Applications,20.",
            publisher = "Springer",
                 note = "Lecture Notes in Computer Science, v.12253",
             keywords = "Growing Self-Organized Map · Land use and cover · Machine 
                         learning.",
             abstract = "Mapping Earth land use and cover changes is crucial to understand 
                         agricultural dynamics. Recently, analysis of time series extracted 
                         from Earth observation satellite images has been widely used to 
                         produce land use and cover information. In time series analysis, 
                         clustering is a common technique performed to discover patterns on 
                         data sets. In this work, we evaluate the Growing Self-Organizing 
                         Maps algorithm for clustering satellite image time series and 
                         compare it with Self-Organizing Maps algorithm. This paper 
                         presents a case study using satellite image time series associated 
                         to samples of land use and cover classes, highlighting the 
                         advantage of providing a neutral factor (called spread factor) as 
                         a parameter for GSOM, instead of the SOM grid size.",
  conference-location = "Cagliari, Italy",
      conference-year = "01-04 July",
                  doi = "10.1007/978-3-030-58814-4_19",
                  url = "http://dx.doi.org/10.1007/978-3-030-58814-4_19",
                 isbn = "978-303058813-7",
                 issn = "03029743",
             language = "en",
           targetfile = "adeu_assessing.pdf",
        urlaccessdate = "20 maio 2024"
}