@InProceedings{SchultzImmiFormAtzb:2015:ObCrCl,
               author = "Schultz, Bruno and Immitzer, Markus and Formaggio, Ant{\^o}nio 
                         Roberto and Atzberger, Clement",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Object-based crop classification using multitemporal OLI imagery 
                         and Chain Classification with Random Forest",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "3059--3066",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The use of more than one Landsat-like data sensor to automatically 
                         classify different crops is still a challenge. Improvements have 
                         been made using different images to map crops for large areas. The 
                         chain classification (CC) has permitted the use of samples in the 
                         overlapping area (between two Landsat-like images) to classify 
                         cultures at regional scale with an automatic classification. The 
                         Random Forest (RF) model is an automatic ensemble learning 
                         classifier with possible feature selection. RF can also provide 
                         reliability measures of the classification results for each 
                         segment. The goal of this work was to analyze the sugarcane 
                         classification in South of S{\~a}o Paulo State, using 
                         object-based approach, multitemporal images, random forest and 
                         chain classification. In the first step the images from 
                         August/2013 and January/2014 (221/76 and 222/76) were segmented 
                         and reference samples were manually selected from MCC (medium 
                         cycle crop), SCC (short cycle crop), LCC (long cycle crop), Water 
                         body (WB) and others (OT) to generate the first RF model 
                         (M1=overlapping). In the Second step we extracted the samples with 
                         high majority difference from the RFM1 model. After that, the best 
                         samples were used to classify each image in the second model 
                         (M2=221/76) and third model (M3=222/76). The obtained overall 
                         accuracies (OA) were 77.2 % (221/76) and 73.4 % (222/76). The 
                         results could may be improved if the samples were selected from 
                         low and high majority difference values.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "608",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4ALT",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3JM4ALT",
           targetfile = "p0608.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "27 abr. 2024"
}