@InProceedings{SilvaJúnMarEscBac:2022:NoCoUA,
               author = "Silva, Leandro Henrique Furtado Pinto and J{\'u}nior, Jocival 
                         Dantas Dias and Mari, Jo{\~a}o Fernando and Escarpinati, Mauricio 
                         Cunha and Backes, Andr{\'e} Ricardo",
          affiliation = "School of Computer Science, Federal University of Uberl{\^a}ndia 
                         and School of Computer Science, Federal University of 
                         Uberl{\^a}ndia and Federal University of Vi{\c{c}}osa, Campus 
                         Rio Parana{\'{\i}}ba and School of Computer Science, Federal 
                         University of Uberl{\^a}ndia and School of Computer Science, 
                         Federal University of Uberl{\^a}ndia",
                title = "Non-Linear co-registration in UAVs' images using deep learning",
            booktitle = "Proceedings...",
                 year = "2022",
         organization = "Conference on Graphics, Patterns and Images, 35. (SIBGRAPI)",
             keywords = "image registration, multispectral image, deep learning, precision 
                         agriculture, UAV.",
             abstract = "Unmanned Aerial Vehicles (UAVs) has stood out for assisting, 
                         enhancing, and optimizing agricultural production. Images captured 
                         by UAVs allow a detailed view of the analyzed region since the 
                         flight occurs at low and medium altitudes (50m to 400m). In 
                         addition, there is a wide variety of sensors (RGB cameras, heat 
                         capture sensors, multi and hyperspectral cameras, among others), 
                         each with its own characteristics and capable of producing 
                         different information. In multi-spectral images acquisition, we 
                         use a distinct sensor to capture each image band and at different 
                         time, leading to misalignments. To tackle this problem we propose 
                         to train a deep neural network to predict the vector deformation 
                         fields to perform the registration between bands of a 
                         multi-spectral image. The proposed approach has an accuracy 
                         ranging from 89.90% to 93.79% in the task of estimating the 
                         displacement field between bands. With this field estimated by the 
                         network, it is possible to register between the bands without the 
                         need for manual marking of points.",
  conference-location = "Natal, RN",
      conference-year = "24-27 Oct. 2022",
                  doi = "10.1109/SIBGRAPI55357.2022.9991781",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI55357.2022.9991781",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/47JU5QE",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/47JU5QE",
           targetfile = "backes_9.pdf",
        urlaccessdate = "2024, Apr. 28"
}