@InProceedings{SilvaVibrNico:2023:CoAlLa,
               author = "Silva, Murilo Schramm da and Vibrans, Alexander Christian and 
                         Nicoletti, Adilson Luiz",
          affiliation = "{Universidade Regional de Blumenau (FURB)} and {Universidade 
                         Regional de Blumenau (FURB)} and {Universidade Regional de 
                         Blumenau (FURB)}",
                title = "Backdating of invariant pixels: comparison of algorithms for Land 
                         Use and Land Cover Change (LUCC) detection in the subtropical 
                         brazilian atlantic forest",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e156113",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 20. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Backdating, IR-MAD, SGD, CVA.",
             abstract = "A challenge for the use of medium spatial resolution imagery for 
                         change detection consists of the reduced availability of ground 
                         reference data for previous dates. We compared the accuracy of 
                         invariant area sets, generated by three methods (Iteratively 
                         Reweighted Multivariate Alteration Detection, Change Vector 
                         Analysis and Spectral Gradient Difference) for two periods 
                         (2017-2011 and 2011-2006). The classification of the Landsat-5 TM 
                         image of 2006 was performed using as training data the sets of 
                         points indicated as invariant in the binary maps resulted from the 
                         three methods. Overall accuracy for seven land-use classes was 
                         greater (80,5% and 80,2%) when using training areas achieved by 
                         CVA and SGD, respectively than IR-MAD (76%). Were obtained 
                         accuracies greater than 80% for the forest class. The results 
                         stress that the combination of the IRMAD and SGD is preferable 
                         since the CVA is more time consuming due to the subjective 
                         application of thresholds.",
  conference-location = "Florian{\'o}polis",
      conference-year = "02-05 abril 2023",
                 isbn = "978-65-89159-04-9",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/494UJDE",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/494UJDE",
           targetfile = "156113.pdf",
                 type = "Mudan{\c{c}}a de uso e cobertura da Terra",
        urlaccessdate = "27 abr. 2024"
}