@InProceedings{DiazFeiRotSanHei:2017:SpCoRa,
               author = "Diaz, Pedro Marco Achanccaray and Feitosa, Raul Queiroz and 
                         Rottensteiner, Franz and Sanches, Ieda Del Arco and Heipke, 
                         Christian",
          affiliation = "{} and {} and {} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Spatio-temporal Conditional Random Fields for recognition of 
                         sub-tropical crop types from multi-temporal images",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "2539--2546",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Crop recognition from remote sensing images is a challenging task 
                         due to the dynamic behavior of different crops. The spectral 
                         appearance of a given crop changes over time because it is highly 
                         related to the phenological stage at each epoch or season, making 
                         it necessary to use sequences of images for a correct 
                         classification. Conditional Random Field (CRF) approaches have 
                         been increasingly applied for crop recognition due to their 
                         ability to consider contextual information in both, the spatial 
                         and the temporal domains. This work proposes a spatio-temporal CRF 
                         for modelling different crops and their respective phenological 
                         stages from a sequence of Landsat 5/7 images. The spatial context 
                         is introduced using a contrast-sensitive smooth labeling method. 
                         The interactions in the temporal domain are modeled based on the 
                         joint posterior probability of class relations between adjacent 
                         epochs given the observed data. These class relations are learnt 
                         using a Random Forest (RF) classifier. Comparisons between 
                         mono-temporal classification using RF, CRFs considering only 
                         spatial context information and the proposed model are presented. 
                         Furthermore, an analysis on how the sequence image length as well 
                         as the starting epoch affects the classification accuracy is 
                         carried out. Improvements in the overall accuracy of up to 12% and 
                         6% over the RF and mono-temporal CRF approaches, respectively, are 
                         obtained using the proposed model considering sequences of up to 9 
                         images.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59961",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLQPP",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLQPP",
           targetfile = "59961.pdf",
                 type = "Agricultura e pecu{\'a}ria",
        urlaccessdate = "27 abr. 2024"
}