@InCollection{SilveiraJrCecaSantRibe:2018:ThSpAs,
               author = "Silveira Junior, Carlos R. and Cecatto, Jos{\'e} Roberto and 
                         Santos, Matilde T. P. and Ribeiro, Marcela X.",
                title = "Thematic spatiotemporal association rules to track the evolving of 
                         visual features and their meaning in satellite image time series",
            booktitle = "Advances in intelligent systems and computing",
            publisher = "Springer",
                 year = "2018",
               editor = "Lafiti, S.",
                pages = "317--323",
             keywords = "Image classification · Spatiotemporal association classifier · 
                         Solar data · Thematic spatiotemporal association rules extraction 
                         · Temporal series of images · Temporal series of semantic data.",
             abstract = "Satellite Image Time Series (SITS) is a set of images taken from 
                         the same satellite scene at different times. The mining of SITS is 
                         challenging task because it requires spatiotemporal data analysis. 
                         An example of the need for SITS mining is the analysis of solar 
                         flares and their evolving. Thematic Spatiotemporal Association 
                         Rules (TSARs) are associations that show spatiotemporal 
                         relationships among the values of the thematics attributes. By 
                         employing TSARs, we propose an approach to track the evolving of 
                         visual features of SITS images and their meaning. Our approach, 
                         called Miner of Thematic Spatiotemporal Associations for Images 
                         (MiTSAI), considers the data extracting and transformation, the 
                         thematic spatiotemporal association rule mining (TSARs), and the 
                         post-processing of the mined TSARs, that relate the visual 
                         features and their meaning. Our experiment shows that the proposed 
                         approach improves the domain expert team understanding of Solar 
                         SITS. Moreover, MiTSAI presented an acceptable time performance 
                         being able of extracting and processing TSARs using a long period 
                         of historical data faster than the period needed for the arrival 
                         of new data in the database.",
          affiliation = "{Universidade Federal de S{\~a}o Carlos (UFSCar)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Federal 
                         de S{\~a}o Carlos (UFSCar)} and {Universidade Federal de S{\~a}o 
                         Carlos (UFSCar)}",
                  doi = "10.1007/978-3-319-77028-4_43",
                  url = "http://dx.doi.org/10.1007/978-3-319-77028-4_43",
                 issn = "2194-5357",
             language = "en",
               volume = "738",
        urlaccessdate = "28 abr. 2024"
}