@Article{MausCâSoSaRaQu:2016:TiDyTi,
               author = "Maus, Victor Wegner and C{\^a}mara, Gilberto and Souza, Ricardo 
                         Cartaxo Modesto de and Sanchez, Alber Hermersson Ipia and Ramos, 
                         Fernando Manuel and Queiroz, Gilberto Ribeiro de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "A time-weighted dynamic time warping method for land-use and 
                         land-cover mapping",
              journal = "IEEE Journal of Selected Topics in Applied Earth Observations and 
                         Remote Sensing",
                 year = "2016",
               volume = "9",
               number = "8",
                pages = "3729--3739",
                month = "Aug.",
             keywords = "Dynamic programming, image sequence analysis, monitoring, pattern 
                         classification, time series.",
             abstract = "This paper presents a time-weighted version of the dynamic time 
                         warping (DTW) method for land-use and land-cover classification 
                         using remote sensing image time series. Methods based on DTW have 
                         achieved significant results in time-series data mining. The 
                         original DTW method works well for shape matching, but is not 
                         suited for remote sensing time-series classification. It 
                         disregards the temporal range when finding the best alignment 
                         between two time series. Since each land-cover class has a 
                         specific phenological cycle, a good time-series land-cover 
                         classifier needs to balance between shape matching and temporal 
                         alignment. To that end, we adjusted the original DTW method to 
                         include a temporal weight that accounts for seasonality of 
                         land-cover types. The resulting algorithm improves on previous 
                         methods for land-cover classification using DTW. In a case study 
                         in a tropical forest area, our proposed logistic time-weighted 
                         version achieves the best overall accuracy of 87.32%. The accuracy 
                         of a version with maximum time delay constraints is 84.66%. A 
                         time-warping method without time constraints has a 70.14% 
                         accuracy. To get good results with the proposed algorithm, the 
                         spatial and temporal resolutions of the data should capture the 
                         properties of the landscape. The pattern samples should also 
                         represent well the temporal variation of land cover.",
                  doi = "10.1109/JSTARS.2016.2517118",
                  url = "http://dx.doi.org/10.1109/JSTARS.2016.2517118",
                 issn = "1939-1404 and 2151-1535",
             language = "en",
           targetfile = "maus_a time.pdf",
        urlaccessdate = "01 maio 2024"
}