@InProceedings{FrançaAndVirSiaMai:2017:SePsRe,
               author = "Fran{\c{c}}a, David Guimar{\~a}es Monteiro and Anderson, Liana 
                         Oighenstein and Virgilio, Lucena Rocha and Siani, Sacha Maru{\~a} 
                         Ortiz and Maia, Monique Rodrigues da Silva Andrade",
                title = "Segmentation as a pseudo-spatial resolution optimization method 
                         for Sentinel-2A images applied to sand pit detection in Cruzeiro 
                         do Sul, Acre",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "8000--8007",
         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 = "This work method is proposed to partially optimize the spatial 
                         resolution of Sentinel-2A MSI Aerossol (443nm), Cirrus (945nm) and 
                         SWIR-1(1380nm) image bands from 60 to 10 meters at the expense of 
                         generalizing the spectral information inside pixels of similar DN 
                         (Digital Number) values. The study area is in the municipality of 
                         Cruzeiro do Sul, northwest portion of the Acre state. The proposed 
                         method is based on multiresolution image segmentation and is 
                         already present in the literature commonly used for object-based 
                         image classification purposes. This paper also make use of a 
                         random forest algorithm to classify two different images from the 
                         study site in order to detect sand pit extraction sites called 
                         Canchas. That Canchas can be harmful to the environment by not 
                         allowing the native vegetation to regenerate and also polluting 
                         water springs in the nearby areas. Preliminary results show that 
                         the proposed method is suitable for semi-automatic image 
                         classification purposes with satisfactory results and can also be 
                         implemented in the optimization of remote sensing images spatial 
                         resolution accomplishing this objective. Furthermore, the random 
                         forest classification model displayed good generalization power by 
                         being trained with samples of only one of the images (2016-10-30) 
                         and even so, being able to detect the sand pit areas in both 
                         images.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59486",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMGQ5",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMGQ5",
           targetfile = "59486.pdf",
                 type = "Degrada{\c{c}}{\~a}o de florestas",
        urlaccessdate = "27 abr. 2024"
}