@Article{MoreiraReKöDuCaAr:2020:SuAnMO,
               author = "Moreira, Noeli Aline Particcelli and Reis, Mariane Souza and 
                         K{\"o}rting, Thales Sehn and Dutra, Luciano Vieira and Castejon, 
                         Emiliano Ferreira and Arai, Eg{\'{\i}}dio",
          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)}",
                title = "Subpixel analysis of MODIS imagery time series using transfer 
                         learning and relative calibration",
              journal = "Revista Brasileira de Cartografia",
                 year = "2020",
               volume = "72",
               number = "4",
                pages = "558--573",
             keywords = "Relative Calibration.Image Time-series.Samples Extension.Subpixel 
                         Analysis. Land Cover classification.",
             abstract = "Transfer learning reuses a pre-trained model on a new related 
                         problem, which can be useful for monitoring large areas such as 
                         the Amazon biome. A given object must havesimilar spectral 
                         characteristics in the data usedfor this type of analysis, which 
                         can be achieved usingrelative calibration techniques. In this 
                         article, we present a relative calibration process in 
                         multitemporal images and evaluate its impacts on a subpixel 
                         classification process. MODIS images from the Amazon region, 
                         collected between 2013and 2017, were relatively calibrated using a 
                         2012 image as reference and classified by transfer learning. 
                         Classifications of calibrated and uncalibrated images were 
                         compared with data from the PRODES project, focusing on forest 
                         areas. A great variation was observed in the spectral responses of 
                         the forest class, even in images of proximatedates and fromthe 
                         same sensor. These variations significantly impacted the land 
                         cover classifications in the subpixel, with cases of agreement 
                         between the uncalibrated data maps and PRODES of 0%. For 
                         calibrated data, the agreement values were greater than 70%. The 
                         results indicate that the method used, although quite simple, is 
                         adequate and necessary for the subpixel classification of MODIS 
                         images by transfer learning.",
                  doi = "10.14393/rbcv72n4-54044",
                  url = "http://dx.doi.org/10.14393/rbcv72n4-54044",
                 issn = "0560-4613 and 1808-0936",
                label = "lattes: 1175464822052393 2 MoreiraReK{\"o}DuCaAr:2020:SuAnMO",
             language = "en",
           targetfile = "moreira_subpixel.pdf",
                  url = "http://www.seer.ufu.br/index.php/revistabrasileiracartografia/article/view/54044",
        urlaccessdate = "27 abr. 2024"
}