@InProceedings{SousaSouzZaneCarv:2012:AnRaRe,
               author = "Sousa, C{\'e}lio Helder Resende and Souza, Carolina Gusm{\~a}o 
                         and Zanella, Lisiane and Carvalho, Luis Marcelo Tavares de",
                title = "Analysis of RapidEye´s red edge band for image segmentation and 
                         classification",
            booktitle = "Proceedings...",
                 year = "2012",
               editor = "Feitosa, Raul Queiroz and Costa, Gilson Alexandre Ostwald Pedro da 
                         and Almeida, Cl{\'a}udia Maria de and Fonseca, Leila Maria Garcia 
                         and Kux, Hermann Johann Heinrich",
                pages = "518--523",
         organization = "International Conference on Geographic Object-Based Image 
                         Analysis, 4. (GEOBIA).",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "Multiresolution Segmentation, Land cover, Decision Tree, Accuracy, 
                         Attributes.",
             abstract = "The objective of this study was to evaluate if a multi-resolution 
                         segmentation algorithm is sensitive to the RapidEyes Red Edge band 
                         and its benefits for vegetation mapping using GEOBIA and machine 
                         learning. We used a high-resolution multi-spectral RapidEye image 
                         taken in June, 2010. This image was segmented with a 
                         multiresolution segmentation algorithm (MRIS) using a fine scale 
                         parameter (300) and thirteen different weights (from 0 to 100) 
                         were assigned to the Red Edge spectral band to evaluate its 
                         influence in the segmentation and classification process. Each 
                         weight generated a segmented image. Attributes related to spectral 
                         information, geometry and texture were calculated for each image 
                         segment using the eCognition Developer®. Visual interpretation was 
                         performed along with field data to select seven classes (Dense 
                         vegetation, Meadow, Mining area, Bare land, Rock outcrop, Urban 
                         area and Water). A sample of 800 objects described by its 
                         attributes was selected from each segmented image. A decision tree 
                         approach based on CART was applied to the samples to select the 
                         attributes that provides the best separation among the classes 
                         within the scene. An accuracy assessment for the classification 
                         using CART was performed to compare the different weights assigned 
                         to the Red edge spectral band. Results showed that the Red Edge 
                         channel had no significant influence on the segmentation process. 
                         The attributes importance rank showed that the index derived from 
                         Red Edge channel can be used as input for image classification.",
  conference-location = "Rio de Janeiro",
      conference-year = "May 7-9, 2012",
                 isbn = "978-85-17-00059-1",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP8W/3BTFEEE",
                  url = "http://urlib.net/ibi/8JMKD3MGP8W/3BTFEEE",
           targetfile = "137.pdf",
                 type = "Segmentation",
        urlaccessdate = "04 jun. 2024"
}