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@InProceedings{BendiniGKMTESF:2015:EfImFu,
               author = "Bendini, Hugo do Nascimento and Girolamo Neto, Cesare Di and 
                         Korting, Thales Sehn and Marujo, Rennan de Freitas Bezerra and 
                         Trabaquini, Kleber and Eberhardt, Isaque Daniel Rocha and Sanches, 
                         Ieda Del Arco and Fonseca, Leila Maria Garcia",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {} and {} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Effects of Image Fusion Methods on Sugarcane Classification with 
                         Landsat-8 Imagery",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "2498--2505",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The culture of sugarcane has great importance in the brazilian 
                         agribusiness. Remote sensing images have been tradicionally used 
                         on manual mapping of sugarcane fields. Manual classification is a 
                         laborious and time-consuming task, especially given the size of 
                         the territory, and it is still necessary to assess the quality of 
                         the maps. Image fusion can improve the identification and mapping 
                         of surface features. The computational data mining methodology 
                         demonstrates high potential for application in areas related to 
                         crop mapping and several classification techniques can be used. 
                         Most studies on fusion of remote sensing images have focused on 
                         the analysis of spectral and spatial quality of the products 
                         obtained by different algorithms, however, once classification is 
                         applied on these products, it is important to analyze the impact 
                         of fusion in the classification. In the literature there are few 
                         studies on this topic, especially considering the Landsat-8. In 
                         this context, we evaluated five pansharpening methods - 
                         Intensity-Hue-Saturation (IHS), Principal Components (PC), 
                         Gran-Schmidt (GS), Discrete Wavelet Transform (DWT) and DWT+IHS 
                         for the classification of sugarcane fields in a Landsat-8 image 
                         (bands 4, 5 and 6). The Support Vector Machine (SVM) algorithm was 
                         used to perform a target detection of sugarcane, using a binary 
                         classification. The samples used were selected based on a field 
                         survey realized on the study area. The best fusion techniques were 
                         the DWT+IHS, DWT and IHS, which obtained higher Universal Image 
                         Quality Index (UIQI) and Spatial Relative Dimensionless Global 
                         Error in Synthesis (SERGAS) values. However, considering the 
                         effects on classification, the GS fusion showed better results 
                         than other methods.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "504",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4A4E",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM4A4E",
           targetfile = "p0504.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "04 dez. 2020"
}


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