@Article{LaRosaFeiHapSanCos:2019:CoDeLe,
               author = "La Rosa, Laura Elena and Feitosa, Raul Queiroz and Happ, Patrick 
                         Nigri and Sanches, Ieda Del'Arco and Costa, Gilson Alexandre 
                         Ostwald Pedro",
          affiliation = "{Pontif{\'{\i}}cia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade Cat{\'o}lica do 
                         Rio de Janeiro (PUC-Rio)} and {Pontif{\'{\i}}cia Universidade 
                         Cat{\'o}lica do Rio de Janeiro (PUC-Rio)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Universidade Estadual do Rio 
                         de Janeiro (UFRJ)}",
                title = "Combining deep learning and prior knowledge for crop mapping in 
                         tropical regions from multitemporal SAR image sequences",
              journal = "Remote Sensing",
                 year = "2019",
               volume = "11",
               number = "17",
                pages = "e2029",
             keywords = "crop mapping, tropical agriculture, SAR, deep learning, 
                         Sentinel-1, multitemporal image analysis.",
             abstract = ": Accurate crop type identification and crop area estimation from 
                         remote sensing data in tropical regions are still considered 
                         challenging tasks. The more favorable weather conditions, in 
                         comparison to the characteristic conditions of temperate regions, 
                         permit higher flexibility in land use, planning, and management, 
                         which implies complex crop dynamics. Moreover, the frequent cloud 
                         cover prevents the use of optical data during large periods of the 
                         year, making SAR data an attractive alternative for crop mapping 
                         in tropical regions. This paper evaluates the effectiveness of 
                         Deep Learning (DL) techniques for crop recognition from multi-date 
                         SAR images from tropical regions. Three DL strategies are 
                         investigated: autoencoders, convolutional neural networks, and 
                         fully-convolutional networks. The paper further proposes a 
                         post-classification technique to enforce prior knowledge about 
                         crop dynamics in the target area. Experiments conducted on a 
                         Sentinel-1 multitemporal sequence of a tropical region in Brazil 
                         reveal the pros and cons of the tested methods. In our 
                         experiments, the proposed crop dynamics model was able to correct 
                         up to 16.5% of classification errors and managed to improve the 
                         performance up to 3.2% and 8.7% in terms of overall accuracy and 
                         average F1-score, respectively.",
                  doi = "10.3390/rs11172029",
                  url = "http://dx.doi.org/10.3390/rs11172029",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-11-02029-v2.pdf",
        urlaccessdate = "27 abr. 2024"
}