@Article{WuPattSantVija:2014:ToPrMa,
               author = "Wu, Y. and Patterson, A. and Santos, Rafael Duarte Coelho dos and 
                         Vijaykumar, Nandamudi Lankalapalli",
          affiliation = "Coastal and Marine Research Centre, University College Cork and 
                         Coastal and Marine Research Centre, University College Cork and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Topology preserving mapping for maritime anomaly detection",
              journal = "Lecture Notes in Computer Science",
                 year = "2014",
               volume = "8584 LNCS",
               number = "PART 6",
                pages = "313--326",
             keywords = "Topology, Anomaly detection, European Space Agency, Probability 
                         estimator, Recognized maritime picture, Topology-preserving 
                         mappings, Unsupervised learning method, Mapping.",
             abstract = "In this paper, we present the topology preserving mapping for 
                         maritime anomaly detection. Specifically, the topology preserving 
                         mapping is applied as an unsupervised learning method, which 
                         captures the vessel behaviors and visualizes the extracted 
                         underlying data structure. At the same time, the topology 
                         preserving mapping is used as the probability estimator, where the 
                         data likelihood can be evaluated and the anomalies can be 
                         detected. Real satellite AIS data, used by the Next Generation 
                         Recognized Maritime Picture project (NG-RMP) funded by the 
                         European Space Agency, is used in this paper as the main data 
                         source. We demonstrate that the topology preserving mapping can 
                         classify the vessel observations and detect the anomalies 
                         reasonably and with high accuracy.",
                  doi = "10.1007/978-3-319-09153-2_24",
                  url = "http://dx.doi.org/10.1007/978-3-319-09153-2_24",
                 isbn = "9783319091525",
                 issn = "0302-9743",
                label = "scopus 2014-11 WuPattSantVija:2014:ToPrMa",
             language = "en",
        urlaccessdate = "07 maio 2024"
}