@Article{SoaresKörtFonsBend:2020:SiNoIt,
               author = "Soares, Anderson Reis and K{\"o}rting, Thales Sehn and Fonseca, 
                         Leila Maria Garcia and Bendini, Hugo do Nascimento",
          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 = "Simple nonlinear iterative temporal clustering",
              journal = "IEEE Transactions on Geoscience and Remote Sensing",
                 year = "2020",
               volume = "1",
               number = "1",
                pages = "1--11",
             abstract = "Classifying dense satellite image time series has become a 
                         necessity, especially with the recent efforts to create analysis 
                         ready data cubes. Approaches developed to perform this task are 
                         usually pixel-based. Even though these approaches can achieve good 
                         results, they do not take advantage of the intrinsic spatial 
                         correlation of geographic data nor do they consider spatial 
                         heterogeneity along with the time series. Region-based 
                         classification is a suitable solution to incorporate contextual 
                         information for dense satellite image time series classification. 
                         In this article, we introduce a new segmentation method based on a 
                         superpixel approach. This method creates multitemporal 
                         superpixels, which are meaningful regions in space and time. To 
                         evaluate the performance of the proposed method, tests were 
                         performed on two data sets using a total of 23 ground-truth 
                         references. Experimental results showed that the method performed 
                         well, achieving a good boundary agreement and obtaining high 
                         scores on the three metrics used for evaluation.",
                  doi = "10.1109/TGRS.2020.3033266",
                  url = "http://dx.doi.org/10.1109/TGRS.2020.3033266",
                 issn = "0196-2892",
                label = "lattes: 5123287769635741 3 SoaresK{\"o}rtFonsBend:2020:SiNoIt",
             language = "pt",
           targetfile = "soares_simple.pdf",
        urlaccessdate = "20 maio 2024"
}