@Article{MartinezLaRFeiSanHap:2021:FuCoRe,
               author = "Martinez, Jorge Andres Chamorro and La Rosa, Laura Elena Cu{\'e} 
                         and Feitosa, Raul Queiroz and Sanches, Ieda Del'Arco and Happ, 
                         Patrick Nigri",
          affiliation = "{Pontificia Universidade Cat{\'o}lica do Rio de Janeiro 
                         (PUC-Rio)} and {Pontificia Universidade Cat{\'o}lica do Rio de 
                         Janeiro (PUC-Rio)} and {Pontificia Universidade Cat{\'o}lica do 
                         Rio de Janeiro (PUC-Rio)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Pontificia Universidade Cat{\'o}lica do 
                         Rio de Janeiro (PUC-Rio)}",
                title = "Fully convolutional recurrent networks for multidate crop 
                         recognition from multitemporal image sequences",
              journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
                 year = "2021",
               volume = "171",
                pages = "188--201",
                month = "Jan.",
             keywords = "Convolutional recurrent networks, Fully convolutional networks, 
                         Recurrent networks, Crop recognition, Deep learning, Remote 
                         sensing.",
             abstract = "Crop recognition in tropical regions is a challenging task because 
                         of the highly complex crop dynamics, with multiple crops per year. 
                         Nevertheless, most automatic methods proposed thus far are devoted 
                         to temperate areas where normally a single crop is cultivated 
                         along the crop year. This paper introduces convolutional recurrent 
                         networks for crop recognition in areas characterized by complex 
                         spatiotemporal dynamics typical of tropical agriculture, where a 
                         per date classification is required. The proposed networks consist 
                         of two sequential steps. First, a deep network simultaneously 
                         models spatial and temporal contexts. Second, a post-processing 
                         algorithm enforces prior knowledge about the crop dynamics in the 
                         target area based on the posterior probabilities computed in the 
                         first step. The paper proposes deep network architectures that 
                         join a fully convolutional network (FCN) for modeling spatial 
                         context at multiple levels and a bidirectional recurrent neural 
                         network to explore the temporal context. The recurrent network is 
                         configured as N-to-N, where N is the sequence length. This allows 
                         it to produce classification outcomes for the entire sequence of 
                         multi-temporal images using a single network. Different network 
                         designs are proposed based on three FCN architectures: U-Net, 
                         dense network, and Atrous Spatial Pyramid Pooling. A convolutional 
                         Long-Short-Term-Memory (ConvLSTM) accounts for sequence modeling, 
                         whereas the Most Likely Class Sequence (MLCS) algorithm is adopted 
                         for enforcing prior knowledge. The paper finally reports 
                         experiments conducted on Sentinel-1 data of two publicly available 
                         datasets from different tropical regions. The experimental results 
                         indicated that the proposed architectures outperformed 
                         state-of-the-art methods based on recurrent networks in terms of 
                         Overall Accuracy and per-class F1 score.",
                  doi = "10.1016/j.isprsjprs.2020.11.007",
                  url = "http://dx.doi.org/10.1016/j.isprsjprs.2020.11.007",
                 issn = "0924-2716",
             language = "en",
           targetfile = "martinez_fully.pdf",
        urlaccessdate = "20 maio 2024"
}