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@InProceedings{ArabiFernPizaPinh:2015:TySeCl,
               author = "Arabi, Samir and Fernandes, David and Pizarro, Marco Ant{\^o}nio 
                         and Pinho, Marcelo",
          affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e 
                         Tecnologia de Go{\'{\i}}as and {Instituto Tecnol{\'o}gico de 
                         Aeron{\'a}utica (ITA)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Tecnol{\'o}gico de 
                         Aeron{\'a}utica (ITA)}",
                title = "Typical sequence classification method in hyperspectral images 
                         with reduced bands",
            booktitle = "Proceedings...",
                 year = "2015",
         organization = "IEEE International Geoscience and Remote Sensing Symposium 
                         (IGARSS)",
             keywords = "Typical Sequence, Hyperspectral, Image Classification, HMM.",
             abstract = "This work presents a new method for hyperspectral spectra 
                         classification based on the Typical Sequence (TS) derived from the 
                         Asymptotic Equipartition Theorem and Information Theory. Each 
                         Endmember (EM) of a scene is represented by a Hidden Markov Model 
                         (HMM) and a spectrum is classified in a given class if it can be 
                         considered a TS generated by the HMM associated with the EM 
                         related to the class. The Discrete Wavelet Transform (DWT) is used 
                         in the orthogonal decomposition of the original spectrum and the 
                         HMM parameters are estimated using this orthogonal decomposition. 
                         The proposed method is tested with AVIRIS spectra of a scene with 
                         13 EM and the classification results show that 32 spectral bands 
                         can be used instead of the original 209 bands, without significant 
                         loss in the classification process.",
  conference-location = "Milan, Italy",
      conference-year = "23-31 July",
        urlaccessdate = "04 dez. 2020"
}


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