@Article{LucenaBreuKux:2022:ApLaUn,
               author = "Lucena, Felipe Rafael de S{\'a} Menezes and Breunig, F{\'a}bio 
                         Marcelo and Kux, Hermann Johann Heinrich",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de Santa Maria (UFSM)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "The Combined Use of UAV-Based RGB and DEM Images for the Detection 
                         and Delineation of Orange Tree Crowns with Mask R-CNN: An Approach 
                         of Labeling and Unified Framework",
              journal = "Future Internet",
                 year = "2022",
               volume = "14",
               number = "10",
                pages = "e275",
                month = "Oct.",
             keywords = "instance segmentation, Mask R-CNN, precision agriculture, tree 
                         delineation, tree detection, UAV-based images.",
             abstract = "In this study, we used images obtained by Unmanned Aerial Vehicles 
                         (UAV) and an instance segmentation model based on deep learning 
                         (Mask R-CNN) to evaluate the ability to detect and delineate 
                         canopies in high density orange plantations. The main objective of 
                         the work was to evaluate the improvement acquired by the 
                         segmentation model when integrating the Canopy Height Model (CHM) 
                         as a fourth band to the images. Two models were evaluated, one 
                         with RGB images and the other with RGB + CHM images, and the 
                         results indicated that the model with combined images presents 
                         better results (overall accuracy from 90.42% to 97.01%). In 
                         addition to the comparison, this work suggests a more efficient 
                         ground truth mapping method and proposes a methodology for 
                         mosaicking the results by Mask R-CNN on remotely sensed images.",
                  doi = "10.3390/fi14100275",
                  url = "http://dx.doi.org/10.3390/fi14100275",
                 issn = "1999-5903",
             language = "en",
           targetfile = "futureinternet-14-00275.pdf",
        urlaccessdate = "08 maio 2024"
}