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@Article{DiazFeitSancCost:2016:MeEsTe,
               author = "Diaz, P. M. A. and Feitosa, Raul Q. and Sanches, Ieda Del'Arco and 
                         Costa, G. A. O. P.",
          affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-RJ)} and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do 
                         Rio de Janeiro (PUC-RJ)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade Estadual do Rio de Janeiro 
                         (UERJ)}",
                title = "A method to estimate temporal interaction in a conditional random 
                         field based approach for crop recognition",
              journal = "International Archives of the Photogrammetry, Remote Sensing and 
                         Spatial Information Sciences",
                 year = "2016",
               volume = "41",
               number = "B7",
                pages = "205--211",
             keywords = "Conditional Random Fields, Crop Recognition, Multitemporal Image 
                         Analysis.",
             abstract = "This paper presents a method to estimate the temporal interaction 
                         in a Conditional Random Field (CRF) based approach for crop 
                         recognition from multitemporal remote sensing image sequences. 
                         This approach models the phenology of different crop types as a 
                         CRF. Interaction potentials are assumed to depend only on the 
                         class labels of an image site at two consecutive epochs. In the 
                         proposed method, the estimation of temporal interaction parameters 
                         is considered as an optimization problem, whose goal is to find 
                         the transition matrix that maximizes the CRF performance, upon a 
                         set of labelled data. The objective functions underlying the 
                         optimization procedure can be formulated in terms of different 
                         accuracy metrics, such as overall and average class accuracy per 
                         crop or phenological stages. To validate the proposed approach, 
                         experiments were carried out upon a dataset consisting of 12 
                         co-registered LANDSAT images of a region in southeast of Brazil. 
                         Pattern Search was used as the optimization algorithm. The 
                         experimental results demonstrated that the proposed method was 
                         able to substantially outperform estimates related to joint or 
                         conditional class transition probabilities, which rely on training 
                         samples.",
                  doi = "10.5194/isprs-archives-XLI-B7-205-2016",
                  url = "http://dx.doi.org/10.5194/isprs-archives-XLI-B7-205-2016",
                 issn = "1682-1750",
                label = "lattes: 2456184661855977 3 DiazFeitSancCost:2016:MEESTE",
             language = "en",
           targetfile = "diaz_method.pdf",
        urlaccessdate = "27 nov. 2020"
}


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