@InProceedings{BernardesFonsMoreCama:2009:EsReSe,
               author = "Bernardes, Tiago and Fonseca, Leila Maria Garcia and Moreira, 
                         Mauricio Alves and Camargo, Fl{\'a}vio Fortes",
          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}",
                title = "Estrutura{\c{c}}{\~a}o de redes sem{\^a}nticas na 
                         classifica{\c{c}}{\~a}o orientada a objeto de imagens orbitais 
                         para mapeamento de {\'a}reas cafeeiras",
            booktitle = "Anais...",
                 year = "2009",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "6789--6796",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 14. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "coffee, image segmentation, object-based image analysis, 
                         caf{\'e}, segmenta{\c{c}}{\~a}o de imagens, an{\'a}lise 
                         orientada a objeto.",
             abstract = "Mapping coffee lands by using intermediate spatial resolution 
                         imagery has been a challenge for remote sensing researchers. 
                         Several environmental, physiological and crop management factors, 
                         increase confusion in both visual interpretation and automatic 
                         classification of coffee fields relative to other landcover units. 
                         It is essential to combine spectral information from the satellite 
                         imagery with ancillary data, such as environmental factors and 
                         contextual interpretation elements (e.g. the degree and kind of 
                         texture and shape conditions) transposed to digital analysis. This 
                         study shows the outcomes of Object Based Image Analysis (OBIA) of 
                         coffee lands supported by ancillary data. Non supervised 
                         classification (the ISOSEG algorithm available in the SPRING 
                         software) of the same area using Landsat Thematic Mapper (TM) 
                         images was carried out in order to compare the results. A 
                         reference map was generated by on-screen digitalization of Landsat 
                         imagery. They were selected on-screen several features to improve 
                         the land cover classification through a semantic net. The 
                         object-oriented classification performed better, in terms of 
                         accuracy, than the non supervised classification. The highest 
                         overall accuracy was 83%. However, some types of coffee crops 
                         could not be identified only by expert knowledge based selection 
                         of features, so it is advisable to use a formal method, such as 
                         data mining, to well select the best thresholds and the features 
                         suitable to identify those types of coffee crops.",
  conference-location = "Natal",
      conference-year = "25-30 abr. 2009",
                 isbn = "978-85-17-00044-7",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "dpi.inpe.br/sbsr@80/2008/11.17.19.10",
                  url = "http://urlib.net/ibi/dpi.inpe.br/sbsr@80/2008/11.17.19.10",
           targetfile = "6789-6796.pdf",
                 type = "Processamento de Imagens",
        urlaccessdate = "15 jun. 2024"
}