@Article{SimġesCQSASCF:2021:SaImTi,
               author = "Sim{\~o}es, Rolf Ezequiel de Oliveira and Camara, Gilberto and 
                         Queiroz, Gilberto Ribeiro de and Souza, Felipe and Andrade, Pedro 
                         Ribeiro de and Santos, Lorena Alves dos and Carvalho, Alexandre 
                         and Ferreira, Karine Reis",
          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)} and 
                         {Instituto de Pesquisas Economicas e Aplicadas (IPEA)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Satellite Image Time Series Analysis for Big Earth Observation 
                         Data",
              journal = "Remote Sensing",
                 year = "2021",
               volume = "13",
               number = "13",
                pages = "e2428",
                month = "June",
             keywords = "big Earth observation data, data cubes, satellite image time 
                         series, machine learning and deep learning for remote sensing, R 
                         package.",
             abstract = "The development of analytical software for big Earth observation 
                         data faces several challenges. Designers need to balance between 
                         conflicting factors. Solutions that are efficient for specific 
                         hardware architectures can not be used in other environments. 
                         Packages that work on generic hardware and open standards will not 
                         have the same performance as dedicated solutions. Software that 
                         assumes that its users are computer programmers are flexible but 
                         may be difficult to learn for a wide audience. This paper 
                         describes sits, an open-source R package for satellite image time 
                         series analysis using machine learning. To allow experts to use 
                         satellite imagery to the fullest extent, sits adopts a time-first, 
                         space-later approach. It supports the complete cycle of data 
                         analysis for land classification. Its API provides a simple but 
                         powerful set of functions. The software works in different cloud 
                         computing environments. Satellite image time series are input to 
                         machine learning classifiers, and the results are post-processed 
                         using spatial smoothing. Since machine learning methods need 
                         accurate training data, sits includes methods for quality 
                         assessment of training samples. The software also provides methods 
                         for validation and accuracy measurement. The package thus 
                         comprises a production environment for big EO data analysis. We 
                         show that this approach produces high accuracy for land use and 
                         land cover maps through a case study in the Cerrado biome, one of 
                         the worlds fast moving agricultural frontiers for the year 2018.",
                  doi = "10.3390/rs13132428",
                  url = "http://dx.doi.org/10.3390/rs13132428",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-13-02428.pdf",
        urlaccessdate = "23 abr. 2024"
}