@InProceedings{SoaresKörtFons:2016:FiExUs,
               author = "Soares, Anderson Reis and K{\"o}rting, Thales Sehn and Fonseca, 
                         Leila Maria Garcia",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "First experiments using the image foresting transform (IFT) 
                         algorithm for segmentation of remote sensing imagery",
            booktitle = "Proceedings...",
                 year = "2016",
         organization = "GEOBIA 2016. : Solutions and Synergies",
            publisher = "University of Twente Faculty of Geo-Information and Earth 
                         Observation (ITC)",
             keywords = "Image Segmentation, Image Foresting Transform, Multiresolution 
                         Segmentation, Segmentation Comparison.",
             abstract = "Image segmentation is a traditional method in Remote Sensing and a 
                         fundamental problem in image processing applications. It has been 
                         widely used, especially with the emergence of the Geographic 
                         Object-Based Image Analysis (GEOBIA). The results of segmentation 
                         must create uniform areas, which must allow a simpler 
                         interpretation by the users and simpler representation for 
                         classification algorithms. Several algorithms were proposed 
                         through the years, using different approaches. One that is widely 
                         used in Remote Sensing applications is the Multiresolution 
                         algorithm, that is based on the region growing method. Other, 
                         which has great potential and is applied in other research areas, 
                         is available on the Image Foresting Transform (IFT) framework, 
                         which has several image operators developed primarily for medical 
                         images. The Watershed from Grayscale Marker operator uses an edge 
                         image to perform the segmentation, however, we propose an 
                         extension of the edge detection algorithm, by summing normalized 
                         gradients of each band. This work aims to evaluate and compare 
                         these two segmentation algorithms, by comparing their results 
                         through supervised segmentation from reference regions, that were 
                         defined manually by an expert user. Quality measures were 
                         evaluated by four metrics, that represent the positional 
                         adjustment based the center of gravity, intensities, size, and the 
                         amount of overlap between the segment created by the algorithms 
                         and the reference segment. We selected 21 objects of a WorldView-2 
                         multispectral image that were used to compute the metrics. Both 
                         methods reached similar results, by comparing the aforementioned 4 
                         metrics applied to the 21 reference regions, IFT achieved better 
                         results for majority of regions. The IFT generated segments with 
                         similar shape when compared with the references, and the 
                         multiresolution generated results with similar sizes and 
                         positional adjustments. It may be explained by the fact that IFT 
                         uses an edge image to perform the segmentation. Both algorithms 
                         obtained similar agreement for intensity.",
  conference-location = "Enschede",
      conference-year = "14-16 sept.",
                  doi = "10.13140/RG.2.2.27778.27844",
                  url = "http://dx.doi.org/10.13140/RG.2.2.27778.27844",
                 isbn = "9789036542012",
                label = "lattes: 5186139934330175 1 SoaresK{\"o}rtFons:2016:FiExUs",
             language = "en",
           targetfile = "soares_first.pdf",
                  url = "http://proceedings.utwente.nl/441/",
        urlaccessdate = "28 abr. 2024"
}