@Article{SchultzImFoSaLuAt:2015:SeSeCl,
               author = "Schultz, Bruno and Immitzer, Markus and Formaggio, Ant{\^o}nio 
                         Roberto and Sanches, Ieda Del Arco and Luiz, Alfredo Jos{\'e} 
                         Barreto and Atzberger, Clement",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and University 
                         of Natural Resources and Life Sciences, Vienna (BOKU) and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Embrapa Meio 
                         Ambiente} and {University of Natural Resources and Life 
                         Sciences}",
                title = "Self-guided segmentation and classification of multi-temporal 
                         Landsat 8 images for crop type mapping in southeastern Brazil",
              journal = "Remote Sensing",
                 year = "2015",
               volume = "7",
               number = "11",
                pages = "14482--14508",
             keywords = "OBIA, crop mapping, Brazil, multi-resolution segmentation, OLI, 
                         random forest.",
             abstract = "Only well-chosen segmentation parameters ensure optimum results of 
                         object-based image analysis (OBIA). Manually defining suitable 
                         parameter sets can be a time-consuming approach, not necessarily 
                         leading to optimum results; the subjectivity of the manual 
                         approach is also obvious. For this reason, in supervised 
                         segmentation as proposed by Stefanski et al. (2013) one integrates 
                         the segmentation and classification tasks. The segmentation is 
                         optimized directly with respect to the subsequent classification. 
                         In this contribution, we build on this work and developed a fully 
                         autonomous workflow for supervised object-based classification, 
                         combining image segmentation and random forest (RF) 
                         classification. Starting from a fixed set of randomly selected and 
                         manually interpreted training samples, suitable segmentation 
                         parameters are automatically identified. A sub-tropical study site 
                         located in S{\~a}o Paulo State (Brazil) was used to evaluate the 
                         proposed approach. Two multi-temporal Landsat 8 image mosaics were 
                         used as input (from August 2013 and January 2014) together with 
                         training samples from field visits and VHR (RapidEye) 
                         photo-interpretation. Using four test sites of 15 × 15 km2 with 
                         manually interpreted crops as independent validation samples, we 
                         demonstrate that the approach leads to robust classification 
                         results. On these samples (pixel wise, n \≈ 1 million) an 
                         overall accuracy (OA) of 80% could be reached while classifying 
                         five classes: sugarcane, soybean, cassava, peanut and others. We 
                         found that the overall accuracy obtained from the four test sites 
                         was only marginally lower compared to the out-of-bag OA obtained 
                         from the training samples. Amongst the five classes, sugarcane and 
                         soybean were classified best, while cassava and peanut were often 
                         misclassified due to similarity in the spatio-temporal feature 
                         space and high within-class variabilities. Interestingly, 
                         misclassified pixels were in most cases correctly identified 
                         through the RF classification margin, which is produced as a 
                         by-product to the classification map.",
                  doi = "10.3390/rs71114482",
                  url = "http://dx.doi.org/10.3390/rs71114482",
                 issn = "2072-4292",
                label = "lattes: 2456184661855977 4 SchultzImFoSaLuAt:2015:SeSeCl",
             language = "en",
           targetfile = "1_schultz.pdf",
        urlaccessdate = "27 abr. 2024"
}