@Article{ZanottaHaer:2012:GrLaCo,
               author = "Zanotta, Daniel Capella and Haertel, Victor",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal do Rio Grande do Sul}",
                title = "Gradual land cover change detection based on multitemporal 
                         fraction images",
              journal = "Pattern Recognition",
                 year = "2012",
               volume = "45",
                pages = "2927--2937",
                month = "Aug.",
             keywords = "Remote sensing, change detection, linear mixture model, spatial 
                         context, unsupervised change detection, remote-sensing images, 
                         change vector analysis, classification, model, transitions, 
                         algorithms, multidate, domain.",
             abstract = "This study proposes a new approach to change detection in remote 
                         sensing multi-temporal image data. Rather than allocating pixels 
                         to one of two disjoint classes (change, no-change) which is the 
                         approach most commonly found in the literature, we propose in this 
                         study to define change in terms of degrees of membership to the 
                         class change. The methodology aims to model images depicting the 
                         natural environment more realistically, taking into account that 
                         changes tend to occur in a continuum rather than being sharply 
                         distinguished. To this end, a sub-pixel approach is implemented to 
                         help detect degrees of change in every pixel. Three experiments 
                         employing the proposed approach using synthetic and real image 
                         data are reported and their results discussed.",
                  doi = "10.1016/j.patcog.2012.02.004",
                  url = "http://dx.doi.org/10.1016/j.patcog.2012.02.004",
                 issn = "0031-3203",
                label = "lattes: 1023733288544345 1 ZanottaHaer:2012:GrLaCo",
             language = "en",
        urlaccessdate = "14 maio 2024"
}