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@InProceedings{SantosFerrPicoCama:2019:SeMaEa,
               author = "Santos, Lorena Alves dos and Ferreira, Karine Reis and Picoli, 
                         Michelle Cristina Ara{\'u}jo and Camara, Gilberto",
          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 = "Self-organizing maps in earth observation data cubes analysis",
            booktitle = "Proceedings...",
                 year = "2019",
         organization = "International Workshop on Self-Organizing Maps and Learning Vector 
                         Quantization, Clustering and Data Visualization, 13.",
            publisher = "Springer",
             keywords = "Time series, Self-organizing maps.",
             abstract = "Earth Observation (EO) Data Cubes infrastructures model 
                         analysis-ready data generated from remote sensing images as 
                         multidimensional cubes (space, time and properties), especially 
                         for satellite image time series analysis. These infrastructures 
                         take advantage of big data technologies and methods to store, 
                         process and analyze the big amount of Earth observation satellite 
                         images freely available nowadays. Recently, EO Data Cubes 
                         infrastructures and satellite image time series analysis have 
                         brought new opportunities and challenges for the Land Use and 
                         Cover Change (LUCC) monitoring over large areas. LUCC have caused 
                         a great impact on tropical ecosystems, increasing global 
                         greenhouse gases emissions and reducing the planets biodiversity. 
                         This paper presents the utility of Self-Organizing Maps (SOM) 
                         neural network method in the process to extract LUCC information 
                         from EO Data Cubes infrastructures, using image time series 
                         analysis. Most classification techniques to create LUCC maps from 
                         satellite image time series are based on supervised learning 
                         methods. In this context, SOM is used as a method to assess land 
                         use and cover samples and to evaluate which spectral bands and 
                         vegetation indexes are best suitable for the separability of land 
                         use and cover classes. A case study is described in this work and 
                         shows the potential of SOM in this application.",
  conference-location = "Barcelona, Spain",
      conference-year = "26-28 jun",
                  doi = "10.1007/978-3-030-19642-4_7",
                  url = "http://dx.doi.org/10.1007/978-3-030-19642-4_7",
                 isbn = "978-3-030-19642-4",
                label = "lattes: 1866854311363027 1 SantosFerrCama:2019:SeMaEa",
             language = "en",
           targetfile = "10.1007@978-3-030-19642-47.pdf",
        urlaccessdate = "03 dez. 2020"
}


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