@InProceedings{AntonioHappCostFeit:2017:UtClVi,
               author = "Antonio, Marcelo Musci Zaib and Happ, Patrick Nigri and Costa, 
                         Gilson A O P and Feitosa, Raul Queiroz",
                title = "Utilizando um cluster virtual com Hadoop como uma ferramenta para 
                         explora{\c{c}}{\~a}o de big data em processamento de imagens 
                         digitais",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "7489--7495",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The amount of available remote sensing (RS) data is increasing at 
                         an extremely rapid pace due to recent advances in Earth 
                         observation technologies. This scenario leads to new challenges 
                         related to the ability to handle huge volumes of data with respect 
                         to computational techniques and resources. In this sense, RS data 
                         processing can be considered a big data problem, and in this 
                         context cloud computing is a trend since it offers a powerful 
                         infrastructure to perform large-scale computing, which is usually 
                         available in a pay-as-you-go model, and alleviates users of the 
                         need to acquire and maintain a complex computing infrastructure. 
                         Although prices currently practiced by cloud infrastructure 
                         providers are reasonably low, the development and testing of 
                         cloud-based platforms is a long work, which may become unfeasible 
                         considering the total costs involved. This work describes a 
                         solution to the problem of the costs involved in the development 
                         of methods based on cloud computing, in particular for RS data 
                         processing tools based on the Hadoop framework. Such a solution is 
                         based on the creation of a configurable virtual cluster on a 
                         single physical machine, installed with the software components 
                         required to run a distributed application. The virtual 
                         infrastructure provided by the solution was used for the 
                         development and testing of extensions of a recently proposed 
                         architecture for the distributed classification of RS data. To 
                         validate the extensions, classification experiments were carried 
                         out on hyperspectral images acquired with the ROSIS sensor, 
                         covering the University of Pavia in Italy.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59373",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMFRD",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMFRD",
           targetfile = "59373.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "27 abr. 2024"
}