@Article{ZanottaZortFerr:2018:SuApSi,
               author = "Zanotta, Daniel Capella and Zortea, Maciel and Ferreira, Matheus 
                         Pinheiro",
          affiliation = "Instituto Nacional de Ci{\^e}ncia, Educa{\c{c}}{\~a}o e 
                         Tecnologia and {IBM Research} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "A supervised approach for simultaneous segmentation and 
                         classification of remote sensing images",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2018",
               volume = "142",
                pages = "162--173",
                month = "Aug.",
             keywords = "Object-based image analysis, Segmentation, Supervised 
                         classification, Multispectral imaging.",
             abstract = "Object-based image classification is recognized as one of the best 
                         strategies to analyze high spatial resolution remote sensing 
                         images. This process includes defining scale parameters to form 
                         regions sharing similar characteristics such as color, texture, or 
                         shape. Traditionally, in an object-based supervised classification 
                         setting the image is classified only after the segmentation 
                         process is completed. However, when the imaged objects on the 
                         ground are heterogeneous and of different sizes, some resulting 
                         segments can be appropriate for classification while others are 
                         over or under-segmented. This may cause partial failure of the 
                         subsequent classification. In this paper, we introduce a 
                         simultaneous approach based on the interception of the 
                         segmentation stage by provisional classification of under-growing 
                         segments. Our proposal is to optimize the classification process 
                         by iteratively updating the labels of previously generated regions 
                         only if the estimated posterior probabilities of the winning 
                         classes in the new segments increase. Experiments with three 
                         multispectral datasets acquired by Landsat-5 TM, QuickBird-II, and 
                         WorldView-3 in rural and urban areas compare traditional 
                         object-based approach based on region growing with the proposed 
                         method using well-established classifiers. Our results show that 
                         the proposed method becomes much less sensitive to the choice of 
                         segmentation parameters and reaches similar, or even better, 
                         classification accuracies.",
                  doi = "10.1016/j.isprsjprs.2018.05.021",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2018.05.021",
                 issn = "0924-2716",
             language = "en",
           targetfile = "zanotta_supervised.pdf",
        urlaccessdate = "25 abr. 2024"
}