author = "Costa, Gilson and Dutra, Luciano Vieira and Uba, Douglas Messias 
                         and Soares, Marinalva and Feitosa, Raul and Rosa, Rafael",
          affiliation = "{Pontificia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-RIO)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Pontificia 
                         Universidade Cat{\'o}lica do Rio de Janeiro (PUC-RIO)} and 
                title = "MultiSeg: a hierarchical segmentation algorithm for radar and 
                         optical data",
                 year = "2015",
         organization = "International Cartographic Conference, 27.",
             keywords = "remote sensing, radar, segmentation.",
             abstract = "This paper describes the underlying algorithm and key 
                         implementation aspects of a hierarchical segmentation program 
                         called MultiSeg Built on a previous development called SegSar, 
                         MultiSeg consists on a specialized segmentation technique devised 
                         for SAR (Synthetic Aperture Radar) and optical imagery. Initially, 
                         images are compressed at different rates creating an image 
                         pyramid, then a region growing procedure is used in combination 
                         with a split and merge technique at the different compression 
                         levels. In sequence, the program processes the image pyramid from 
                         the coarser to the finer compression levels, applying a border 
                         refinement heuristic each time it changes from one level to the 
                         next. MultiSeg was created in the C++ language, with the support 
                         from the open-source library TerraLib. The devised software 
                         architecture permits easy extension of its capabilities. 
                         Additionally, preliminary tests have shown that MultiSeg is 
                         capable of processing large volumes of data efficiently. This 
                         paper also presents an objective evaluation of segmentation 
                         results obtained with MultiSeg, produced through comparing the 
                         automatic segmentation with manually delineated reference 
  conference-location = "Rio de Janeiro, RJ",
      conference-year = "23-28 Aug.",
        urlaccessdate = "28 jan. 2021"