author = "Zanotta, Daniel Capella and Bruzzone, Lorenzo and Bovolo, 
                         Francesca and Shimabukuro, Yosio Edemir",
          affiliation = "National Institute for Science, Education and Technology, Rio 
                         Grande, Brazil and Department of Information Engineering and 
                         Computer Science, University of Trento, Trento, Italy and Center 
                         for Information and Communication Technology, Fondazione Bruno 
                         Kessler, Trento, Italy and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "An adaptive semisupervised approach to the detection of 
                         user-defined recurrent changes in image time series",
              journal = "IEEE Transactions on Geoscience and Remote Sensing",
                 year = "2015",
               volume = "53",
               number = "7",
                pages = "3707--3719",
                month = "Jul.",
             keywords = "Change detection, deforestation, domain adaptation, forest fires, 
                         recurrent change, time series.",
             abstract = "In this paper, we present a novel domain adaptation technique 
                         aimed at providing reliable change detection maps for a series of 
                         image pairs acquired on the same area at different times. The 
                         proposed technique exploits the polar change vector analysis 
                         method and assumes that the reference data for characterizing a 
                         specific change of interest are available only for a pair of 
                         images (source domain). Then, it exploits the knowledge learned 
                         from the source domain and adapts it to other pairs of images 
                         belonging to the time series (target domains) to be analyzed. The 
                         proposed technique is able to handle possible radiometric 
                         differences among images adapting in an unsupervised way the 
                         decision rule estimated on the source domain to the target domains 
                         through variables estimated directly on the target images. The 
                         proposed approach has been applied to two data sets made up of 
                         time series of Landsat Thematic Mapper images. In one case, the 
                         change of interest is related to evolution of deforestation, while 
                         in the other case, it is related to burned area detection. 
                         Experimental results show the effectiveness of the proposed 
                  doi = "10.1109/TGRS.2014.2381645",
                  url = "http://dx.doi.org/10.1109/TGRS.2014.2381645",
                 issn = "0196-2892",
             language = "en",
           targetfile = "zanotta_adaptive.pdf",
        urlaccessdate = "22 jan. 2021"