@InProceedings{BuenoATWLCFEM:2023:LaUsCo,
               author = "Bueno, Inacio T. and Antunes, Jo{\~a}o F. G. and Toro, Ana P. S. 
                         G. D. and Werner, Jo{\~a}o P. S. and Lamparelli, Rubens A. C. and 
                         Coutinho, Alexandre C. and Figueiredo, Gleyce K. D. A. and 
                         Esquerdo, J{\'u}lio C. D. M. and Magalh{\~a}es, Paulo S. G.",
          affiliation = "{Universidade Estadual de Campinas (UNICAMP)} and {Embrapa 
                         Agricultura Digital} and {Universidade Estadual de Campinas 
                         (UNICAMP)} and {Universidade Estadual de Campinas (UNICAMP)} and 
                         {Universidade Estadual de Campinas (UNICAMP)} and {Embrapa 
                         Agricultura Digital} and {Universidade Estadual de Campinas 
                         (UNICAMP)} and {Embrapa Agricultura Digital} and {Universidade 
                         Estadual de Campinas (UNICAMP)}",
                title = "Land use/land cover classification and scale effect analysis for a 
                         multi-temporal superpixe-based segmentation using Planetscope 
                         data",
            booktitle = "Anais...",
                 year = "2023",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de and Sanches, Ieda DelArco",
                pages = "e155527",
         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 = "object-based image analysis, SNIC, scale factor, Google Earth 
                         Engine, Random Forest.",
             abstract = "Land-use land-cover (LULC) classification has long been an 
                         important topic in Earth observation research, frequently 
                         evaluated with recent advances in remote sensing science. This 
                         study evaluated the accuracy and suitability of LULC 
                         classifications based on the scale effect of a multi-temporal 
                         superpixel-based segmentation using PlanetScope (PS) data. We 
                         applied the Simple Non-Iterative Clustering (SNIC) algorithm 
                         testing five scale factors: 20, 50, 80, 110, and 140. We extracted 
                         statistical information of PS bands and vegetation indices from 
                         image-objects as input information for classification. In 
                         addition, segmentation tests were evaluated by analyzing the 
                         variability inside image-objects. Our results showed that the 
                         scale factor of 50 presented the highest accuracy while the scale 
                         factor of 20 returned the poorest. The scale factor of 20 also 
                         created a large number of image-objects inside land parcels, while 
                         scale factors of 110 and 140 merged adjacent areas. Segmentation 
                         evaluation demonstrated that a satisfactory scale factor for 
                         classification is essential once it directly affects the 
                         within-class variability and spoils segmentation suitability. The 
                         evaluation of these classifications has provided important 
                         insights into the effect of the scale factor in high-resolution 
                         imagery.",
  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/495CHRS",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/495CHRS",
           targetfile = "155527.pdf",
                 type = "An{\'a}lise de s{\'e}ries temporais de imagens de 
                         sat{\'e}lite",
        urlaccessdate = "27 jun. 2024"
}