@InProceedings{ZorteaSanZadSchSte:2017:DeInEu,
               author = "Zortea, Maciel and Santos, Marcelo Nery dos and Zadrozny, Bianca 
                         and Schoeninger, Emerson Roberto and Stetz, Cristiano Cardoso",
                title = "Detecting individual eucalyptus crowns in aerial photographs using 
                         template matching and classification",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6749--6756",
         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 = "Collecting and analyzing forest information is critical for 
                         sustainable forest management. Here we propose a method to 
                         automatically detect the location and diameter of tree crowns at 
                         early growth stages in regularly planted forests using very high 
                         spatial resolution RGB imagery acquired by unmanned aerial 
                         vehicles (UAVs). A list of candidate detections is generated 
                         matching the multiscale convolutions of synthetic crown templates 
                         to image objects. Strong matches are filtered using color-based 
                         rules. Local attributes describing the color and spatial 
                         information in small image patches centered in each retained 
                         detection are passed to an off-line trained Random Forest 
                         classifier that assigns a level of confidence to each tree crown 
                         detection. The method is tested on orthorectified RGB mosaics with 
                         a pixel spacing of about 11 cm using circular templates with 
                         diameters in the range 50-200 cm. Experiments at two study sites 
                         containing about 120-day-old plantations of eucalyptus, located in 
                         Southern Brazil, suggest detections accuracies above 90% when 
                         non-overlapping adjacent crowns have a diameter larger than 6 
                         pixels and are surrounded by mixed backgrounds such as exposed 
                         soil and debris from the previous harvest. The automated counts of 
                         trees in 12 footprints of 1,257m2 were within 11% of the visual 
                         estimate, and within 4% when averaged for the study. Examples of 
                         challenging scenarios requiring further methodological 
                         developments are presented. We anticipate that automated tree 
                         crown detection using the proposed prototype algorithm may 
                         complement traditional field-based tree inventory.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59355",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMDFK",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMDFK",
           targetfile = "59355.pdf",
                 type = "Processamento de imagens",
        urlaccessdate = "27 abr. 2024"
}