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@Article{ArabaiFernPizaPinh:2016:HyImCl,
               author = "Arabai, S. Youssif Wehbi and Fernandes, D. and Pizarro, Marco 
                         Antonio and Pinho, M. da Silva",
          affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e 
                         Tecnologia de Goi{\'a}s (IFG) 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 = "Hyperspectral images classification with typical sequences 
                         associated to the endmember",
              journal = "IEEE Latin America Transactions",
                 year = "2016",
               volume = "14",
               number = "7",
                pages = "3102--3109",
                month = "July",
             keywords = "Classification, HMM, Hyperspectral, Typical Sequences, Wavelet.",
             abstract = "This paper presents a new methodology for hyperspectral image 
                         classification based on the definition of typical sets from the 
                         Asymptotic Equipartition Property, an important tool in the field 
                         of information theory. The Endmembers (EM) are decomposed in 
                         orthogonal functions by a discrete wavelet transform and are 
                         modeled as a HMM (Hidden Markov Model). Based on this model, for 
                         each EM, a Typical Sequence set is established. One spectrum is 
                         classified as a member of a specific EM if belongs to its typical 
                         set. It is considered the case in which a class in the 
                         hyperspectral image can be represented by several subclasses and 
                         also the original spectra can be decimated and be used with less 
                         bands in the classification processes. The proposed method is 
                         tested with a set of AVIRIS data and is compared with the 
                         classification performed by Euclidian Distance, Spectral Angle 
                         Mapper (SAM) and Spectral Information Divergence (SID). It is 
                         shown that the proposed classification can be used with a reduced 
                         number of bands and achieves results comparable with other methods 
                         using all bands.",
                  doi = "10.1109/TLA.2016.7587608",
                  url = "http://dx.doi.org/10.1109/TLA.2016.7587608",
                 issn = "1548-0992",
             language = "en",
           targetfile = "arabi_hyperspectral.pdf",
        urlaccessdate = "29 nov. 2020"
}


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