@InProceedings{PereiraSant:2021:SpCoDa,
               author = "Pereira, Matheus Barros and Santos, Jefersson Alex dos",
          affiliation = "{Universidade Federal de Minas Gerais } and {Universidade Federal 
                         de Minas Gerais}",
                title = "ChessMix: Spatial Context Data Augmentation for Remote Sensing 
                         Semantic Segmentation",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "data augmentation,semantic segmentation,remote sensing.",
             abstract = "Labeling semantic segmentation datasets is a costly and laborious 
                         process if compared with tasks like image classification and 
                         object detection. This is especially true for remote sensing 
                         applications that not only work with extremely high spatial 
                         resolution data but also commonly require the knowledge of experts 
                         of the area to perform the manual labeling. Data augmentation 
                         techniques help to improve deep learning models under the 
                         circumstance of few and imbalanced labeled samples. In this work, 
                         we propose a novel data augmentation method focused on exploring 
                         the spatial context of remote sensing semantic segmentation. This 
                         method, ChessMix, creates new synthetic images from the existing 
                         training set by mixing transformed mini-patches across the dataset 
                         in a chessboard-like grid. ChessMix prioritizes patches with more 
                         examples of the rarest classes to alleviate the imbalance 
                         problems. The results in three diverse well-known remote sensing 
                         datasets show that this is a promising approach that helps to 
                         improve the networks' performance, working especially well in 
                         datasets with few available data. The results also show that 
                         ChessMix is capable of improving the segmentation of objects with 
                         few labeled pixels when compared to the most common data 
                         augmentation methods widely used.",
  conference-location = "Gramado, RS, Brazil (virtual)",
      conference-year = "18-22 Oct. 2021",
                  doi = "10.1109/SIBGRAPI54419.2021.00045",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI54419.2021.00045",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/45CQCDL",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/45CQCDL",
           targetfile = "102.pdf",
        urlaccessdate = "2024, May 06"
}