@Article{SilvaViSaCaSjShSa:2020:ImEdEx,
               author = "Silva, Wanessa da and Vijaykumar, Nandamudi Lankalapalli and 
                         Sandri, Sandra Aparecida and Campos Velho, Haroldo Fraga de and 
                         Sjanic, Zoran and Shiguemori, Elcio H. and Saotome, Osamu",
          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)} and {University of LinkĻoping} and {Departamento 
                         de Ci{\^e}ncia e Tecnologia Aeron{\'a}utica (DCTA)} and 
                         {Departamento de Ci{\^e}ncia e Tecnologia Aeron{\'a}utica 
                         (DCTA)}",
                title = "Image edge extraction by artificial intelligence schemes for UAV 
                         autonomous navigation",
              journal = "Proceedings Series of the Brazilian Society of Computational and 
                         Applied Mathematics",
                 year = "2020",
               volume = "7",
               number = "1",
                 note = "Trabalho apresentado no XXXIX CNMAC, Uberl{\^a}ndia - MG, 2019.",
             keywords = "SAR images, UAV autonomous navigation, edge detection, image 
                         processing.",
             abstract = "We present here the application of an image processing strategy 
                         for autonomous navigation in order to estimate the position of an 
                         Unmanned Aerial Vehicle (UAV) equipped with Synthetic Aperture 
                         Radar (SAR) sensors. Patches of the SAR images are compared to 
                         georeferenced satellite optical images, by first applying an image 
                         edge detection algorithm on all the images, and the UAV position 
                         is determined from the correlation matrix obtained from resulting 
                         segmented images. The performance of three edge detectors are 
                         compared: the Canny approach, an artificial neural network (radial 
                         base function), and a fuzzy system.",
                 issn = "2359-0793",
             language = "pt",
           targetfile = "silva_image.pdf",
        urlaccessdate = "27 abr. 2024"
}