@Article{CostaFonKörBenSou:2018:SpSeAp,
               author = "Costa, Wanderson Santos and Fonseca, Leila Maria Garcia and 
                         K{\"o}rting, Thales Sehn and Bendini, Hugo do Nascimento and 
                         Souza, Ricardo Cartaxo Modesto de",
          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)}",
                title = "Spatio-temporal segmentation applied to optical remote sensing 
                         image time series",
              journal = "IEEE Geoscience and Remote Sensing Letters",
                 year = "2018",
               volume = "1",
               number = "99",
                pages = "1--5",
             keywords = "Image segmentation, remote sensing, satelites, Time series 
                         analysis.",
             abstract = "The availability of a large amount of remote sensing data made 
                         Earth Observation increasingly accessible and detailed. High 
                         temporal and spatial resolution sensors are responsible for making 
                         available data sets of time series in unprecedented proportions. 
                         Within this context, the use of efficient segmentation algorithms 
                         of remote sensing imagery represents an important role in this 
                         scenario, because they provide homogeneous regions in space-time 
                         and hence simplify the data set. In addition, the spatio-temporal 
                         segmentation can bring a new way of interpreting data by means of 
                         analyzing contiguous regions in time. This letter describes a 
                         method for image segmentation applied to time series of the Earth 
                         Observation data. We adapted the traditional region growing method 
                         to detect homogeneous regions in space and time. Study cases were 
                         conducted by considering the dynamic time warping algorithm as the 
                         homogeneity criterion to grow regions. Tests on high temporal 
                         resolution image sequences from Moderate Resolution Imaging 
                         Spectroradiometer and Landsat-8 Operational Land Imager vegetation 
                         indices and comparisons with other distance measurements provided 
                         satisfactory outcomes.",
                  doi = "10.1109/LGRS.2018.2831914",
                  url = "http://dx.doi.org/10.1109/LGRS.2018.2831914",
                 issn = "1545-598X",
                label = "lattes: 5123287769635741 2 CostaFonKorBenSou:2018:SpSeAp",
             language = "pt",
           targetfile = "costa_spatio.pdf",
        urlaccessdate = "27 abr. 2024"
}