@Article{OliveiraDutrSant:2022:MePrNa,
               author = "Oliveira, Willian Vieira de and Dutra, Luciano Vieira and 
                         Sant'Anna, Sidnei Jo{\~a}o Siqueira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "A meta-methodology for preserving narrow objects when using 
                         spatial contextual classifiers for remote sensing data",
              journal = "International Journal of Remote Sensing",
                 year = "2022",
               volume = "43",
               number = "18",
                pages = "6741--3765",
             abstract = "In the field of land-cover classification with remote sensing 
                         images, methods that analyse purely the spectral information of 
                         individual pixels generally produce noisy results, due to 
                         salt-and-pepper effects. The use of methods that also incorporate 
                         spatial contextual information into classification, often defined 
                         as contextual or spectral-spatial approaches, is an effective 
                         strategy for reducing the occurrence of punctual noises and, 
                         consequently, improving accuracy. However, contextual methods 
                         still present a critical limitation: an over smoothing performance 
                         on certain classes can cause the loss of details on important 
                         spatial structures. They may overlook salient punctual and linear 
                         objects that can be efficiently classified using purely spectral 
                         information, particularly over areas of rapid class transition. 
                         This issue is commonly observed with the classification of medium 
                         spatial-resolution images that include narrow class structures, 
                         such as rivers and roads. To solve this problem, we present a 
                         strategy for contextual classification that allows adjusting a 
                         trade-off between noise smoothing and the preservation of small 
                         spatial details. The proposed strategy comprises a 
                         meta-methodology, in the sense that it does not depend on specific 
                         pixel-based and contextual classifiers. The meta-methodology for 
                         improving contextual classification methods (Meta-CTX) consists in 
                         performing a separability analysis, at the pixel level, based on 
                         the class membership estimates provided by a pixel-based 
                         classifier. The Meta-CTX analyses the distance between class 
                         membership estimates in order to identify pixels that are expected 
                         to be accurately classified using purely spectral information. The 
                         Meta-CTX preserves the per-pixel classification of these pixels. 
                         It uses spatial contextual information only to classify pixels 
                         that are more susceptible to classification errors. The 
                         experimental results indicate that the Meta-CTX can efficiently 
                         combine noise smoothing with the preservation of small spatial 
                         details in remote sensing image classification.",
                  doi = "10.1080/01431161.2022.2145580",
                  url = "http://dx.doi.org/10.1080/01431161.2022.2145580",
                 issn = "0143-1161",
             language = "en",
        urlaccessdate = "23 maio 2024"
}