@InProceedings{CintraNovRegCosFei:2010:MoAtRa,
               author = "Cintra, D. P. and Novack, T. and Rego., L. F. G. and Costa, G. A. 
                         O. P. and Feitosa, R. Q.",
          affiliation = "Department of Geography, Pontifical Catholic University of Rio de 
                         Janeiro and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         Department of Geography, Pontifical Catholic University of Rio de 
                         Janeiro and Department of Electrical Engineering, Pontifical 
                         Catholic University of Rio de Janeiro and Department of Electrical 
                         Engineering, Pontifical Catholic University of Rio de Janeiro",
                title = "Pimar project - monitoring the atlantic rainforest remnants and 
                         the urban growth of the rio de janeiro city (brazil) through 
                         remote sensing",
            booktitle = "Proceedings...",
                 year = "2010",
               editor = "Coillie, E. A. Addink and F. M. B. Van",
         organization = "Geographic Object-Based Image Analysis (GEOBIA 2010).",
            publisher = "ISPRS Working Groups",
             keywords = "Land Cover Classification, Rainforest Monitoring, Object-Based 
                         Image Analysis, InterIMAGE System.",
             abstract = "The PIMAR Project - Program for Monitoring the Atlantic Rainforest 
                         Environment and Urban Growth of Rio de Janeiro through Remote 
                         Sensing, aims at the development of an operational methodology for 
                         monitoring the land cover dynamics on the borders between Atlantic 
                         rainforest remnant areas and urban areas in the city of Rio de 
                         Janeiro, Brazil. The project will aid the Government of Rio de 
                         Janeiro State in the implementation of actions against aggressions 
                         to those forested areas and in the definition of urban development 
                         and environmental planning policies. The basic input for the 
                         methodology is a sequence of stereo pairs of IKONOS images, from 
                         which both the vertical and horizontal growth of urban areas are 
                         being measured by visual interpretation on a multitemporal basis. 
                         The PIMAR Project is currently evaluating the use of an automatic 
                         classification model as a way to accelerate land cover change 
                         information production to support decision making. This paper 
                         presents the first results obtained when applying the prototype of 
                         the model in the projects test-site. Such classification model has 
                         been developed and tested within the InterIMAGE system, which is 
                         an open-source knowledge and object-based classification system. 
                         The automatic classification model is being elaborated considering 
                         that an user would have only to collect samples of every land 
                         cover class to have, after running the model, the land cover map 
                         delivered. The presented prototype model uses the C4.5 algorithm, 
                         commonly used spectral features and a simple semantic net for 
                         performing the land cover classification of the test-site. The 
                         visual analysis and the global and per-class accuracy indexes 
                         suggest that the automatically made classification is 
                         satisfactorily accurate and has potential for significantly reduce 
                         the photo-interpreters work. A Global Accuracy of 81% was obtained 
                         as well as a Kappa Index of 0.61. Important classes Vegetated 
                         Areas and Urban areas achieved above 75% user and producers 
                         accuracies.",
  conference-location = "Ghent, Belgium",
      conference-year = "2010",
                 issn = "1682-1777",
             language = "en",
           targetfile = "cintra.pdf",
               volume = "38-4/C7",
        urlaccessdate = "12 maio 2024"
}