@InProceedings{SoaresKörtFonsNeve:2020:UnSeMe,
               author = "Soares, Anderson Reis and K{\"o}rting, Thales Sehn and Fonseca, 
                         Leila Maria Garcia and Neves, Alana Kasahara",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "An Unsupervised Segmentation Method For Remote Sensing Imagery 
                         Based On Conditional Random Fields",
            booktitle = "Proceedings...",
                 year = "2020",
                pages = "1--5",
         organization = "IEEE Latin American GRSS \& ISPRS Remote Sensing Conference 
                         (LAGIRS)",
            publisher = "IEEE",
             keywords = "Image segmentation, Remote Sensing, Conditional Random Fields, 
                         GEOBIA.",
             abstract = "Segmentation is a fundamental problem in image processing and a 
                         common operation in Remote Sensing, which has been widely used 
                         especially in Geographic Object-Based Image Analysis (GEOBIA). In 
                         this paper, we propose a new unsupervised segmentation algorithm 
                         based on the Conditional Random Fields (CRF) theory. The method 
                         relies on two levels of information: (1) that comes from an 
                         unsupervised classification with Fuzzy C-Means algorithm; (2) the 
                         8-connected neighbourhood of a pixel. The algorithm was tested on 
                         a WorldView-2 multispectral image, with 2m of spatial resolution. 
                         Results were evaluated using 6 quality measures, and their 
                         performance was compared with other image segmentation algorithms 
                         that are usually applied by the Remote Sensing community. Results 
                         indicate that the proposed algorithm achieved superior overall 
                         performance when compared others, despite some 
                         over-segmentation.",
  conference-location = "Santiago, Chile",
                  doi = "10.1109/LAGIRS48042.2020.9165623",
                  url = "http://dx.doi.org/10.1109/LAGIRS48042.2020.9165623",
                 isbn = "9781728143507",
                label = "lattes: 5687833912469041 4 SoaresK{\"o}rtFonsNeve:2020:UnSeMe",
             language = "en",
           targetfile = "soares_unsupervised.pdf",
        urlaccessdate = "20 maio 2024"
}