@InProceedings{LeiteFeFoDaPaSa:2008:CrTyRe,
               author = "Leite, Paula Beatriz Cerqueira and Feitosa, Raul Queiroz and 
                         Formaggio, Ant{\^o}nio Roberto and Da Costa, Gilson Alexandre 
                         Ostwald Pedro and Pakzad, Kian and Sanches, Ieda Del'Arco",
          affiliation = "{Catholic University of Rio de Janeiro (PUC-Rio)} and {Catholic 
                         University of Rio de Janeiro (PUC-Rio)} and {National Institute 
                         for Space Research (INPE)} and {Catholic University of Rio de 
                         Janeiro (PUC-Rio)} and {Leibnitz University Hannover (IPI)} and 
                         {National Institute for Space Research (INPE)}",
                title = "Crop type recognition based on Hidden Markov Models of plant 
                         phenology",
            booktitle = "Proceedings...",
                 year = "2008",
               editor = "Jung, Cl{\'a}udio Rosito and Walter, Marcelo",
         organization = "Brazilian Symposium on Computer Graphics and Image Processing, 21. 
                         (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "crop type recognition, hidden markov models, remote sensing, 
                         multitemporal analysis, plant phenology.",
             abstract = "This work introduces a Hidden Markov Model (HMM) based technique 
                         to classify agricultural crops. The method recognizes different 
                         crops by analyzing their spectral profiles over a sequence of 
                         satellite images. Different HMMs, one for each of the considered 
                         crop classes, are used to relate the varying spectral response 
                         along the crop cycles with plant phenology. The method assigns for 
                         a given image segment the crop class whose corresponding HMM 
                         presents the highest probability of emitting the observed sequence 
                         of spectral values. Experiments were conducted upon a sequence of 
                         12 previously classified LANDSAT images. The performance of the 
                         proposed multitemporal classification method was compared to that 
                         of a monotemporal maximum likelihood classifier, and the results 
                         indicated a remarkable superiority of the HMM-based method, which 
                         achieved an average of no less than 93% accuracy in the 
                         identification of the correct crop, for sequences of data 
                         containing a single crop class.",
  conference-location = "Campo Grande, MS, Brazil",
      conference-year = "12-15 Oct. 2008",
                  doi = "10.1109/SIBGRAPI.2008.26",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2008.26",
             language = "en",
                  ibi = "6qtX3pFwXQZG2LgkFdY/UMGpr",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/UMGpr",
           targetfile = "leite-CropTypeRecognition.pdf",
        urlaccessdate = "2024, May 02"
}