@InProceedings{AcuñaAndrKim:2012:TeFuSy,
               author = "Acuña, Mauricio Barrera and Andrade, Marco T{\'u}lio Carvalho de 
                         and Kim, Hae Yong",
                title = "Texture-based fuzzy system for rotation-invariant classification 
                         of aerial orthoimage regions",
            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 = "58--63",
         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 = "Aerial image, Texture classification, Fuzzy Logic, Land Use, 
                         Orthoimage.",
             abstract = "Orthoimages are aerial images where feature displacements and 
                         scale variations have been removed. This type of images is widely 
                         used to calculate areas, determine land cover and land use, among 
                         others. This paper introduces a rotation-invariant classification 
                         model for three common orthoimage regions: city, sea and forest 
                         areas, using only texture information (without color information). 
                         Our classification model analyzes small sub-images (for example, 
                         of 20x20 pixels) to determine their region classes. Our model is 
                         based on a Fuzzy Inference System (FIS) constructed over a set of 
                         new rotation-invariant texture features. The features are 
                         extracted using two rotation-invariant versions of the well-known 
                         grayscale co-occurrence matrix (GLCM). Rotation-invariance is a 
                         desirable property of orthoimage classification systems, because 
                         the aerial images can be taken from different angles. We executed 
                         tests on samples from the three regions, including several rotated 
                         versions. These experiments show that our system reaches 100% of 
                         correct classification rate for our image test database. This 
                         correct classification rate is far superior to the rate obtained 
                         using the classical GLCM without the rotation-invariant property. 
                         Our classifier is robust to images that contain small areas that 
                         do not belong to the overall region type. The results demonstrate 
                         that our model offers a reliable rotation-invariant orthoimage 
                         region 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/3BT6KG2",
                  url = "http://urlib.net/ibi/8JMKD3MGP8W/3BT6KG2",
           targetfile = "020.pdf",
                 type = "Feature Extraction",
        urlaccessdate = "14 maio 2024"
}