@Article{SoaresDuCoFeNeDi:2020:MeImLa,
               author = "Soares, Marinalva Dias and Dutra, Luciano Vieira and Costa, Gilson 
                         Alexandre Ostwald Pedro da and Feitosa, Raul Queiroz and Negri, 
                         Rog{\'e}rio Galante and Diaz, Pedro M. A.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Estadual 
                         do Rio de Janeiro (UERJ)} and {Pontif{\'{\i}}cia Universidade 
                         Cat{\'o}lica do Rio de Janeiro (PUC-Rio)} and {Universidade 
                         Estadual Paulista (UNESP)} and {Pontif{\'{\i}}cia Universidade 
                         Cat{\'o}lica do Rio de Janeiro (PUC-Rio)}",
                title = "A meta-methodology for improving land cover and land use 
                         classification with SAR imagery",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "6",
                pages = "e861",
                month = "Mar.",
             keywords = "region-based classification, GEOBIA, SAR classification, LULC 
                         classification, SAR data segmentation, segmentation tuning, 
                         meta-methodologies.",
             abstract = "Per-point classification is a traditional method for remote 
                         sensing data classification, and for radar data in particular. 
                         Compared with optical data, the discriminative power of radar data 
                         is quite limited, for most applications. A way of trying to 
                         overcome these difficulties is to use Region-Based Classification 
                         (RBC), also referred to as Geographical Object-Based Image 
                         Analysis (GEOBIA). RBC methods first aggregate pixels into 
                         homogeneous objects, or regions, using a segmentation procedure. 
                         Moreover, segmentation is known to be an ill-conditioned problem 
                         because it admits multiple solutions, and a small change in the 
                         input image, or segmentation parameters, may lead to significant 
                         changes in the image partitioning. In this context, this paper 
                         proposes and evaluates novel approaches for SAR data 
                         classification, which rely on specialized segmentations, and on 
                         the combination of partial maps produced by classification 
                         ensembles. Such approaches comprise a meta-methodology, in the 
                         sense that they are independent from segmentation and 
                         classification algorithms, and optimization procedures. Results 
                         are shown that improve the classification accuracy from Kappa = 
                         0.4 (baseline method) to a Kappa = 0.77 with the presented method. 
                         Another test site presented an improvement from Kappa = 0.36 to a 
                         maximum of 0.66 also with radar data.",
                  doi = "10.3390/rs12060961",
                  url = "http://dx.doi.org/10.3390/rs12060961",
                 issn = "2072-4292",
             language = "en",
           targetfile = "soares_meta.pdf",
        urlaccessdate = "28 abr. 2024"
}