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@InProceedings{ArabiFernPiza:2015:HMHySp,
               author = "Arabi, Samir Youssif Wehbi and Fernandes, David and Pizarro, Marco 
                         Ant{\^o}nio",
          affiliation = "Instituto Federal de Educa{\c{c}}{\~a}o, Ci{\^e}ncia e 
                         Tecnologia de Go{\'{\i}}as (IFG) and {Instituto Tecnol{\'o}gico 
                         de Aeron{\'a}utica (ITA)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "HMM for hyperspectral spectrum representation and classification 
                         with endmember entropy vectors",
            booktitle = "Proceedings...",
                 year = "2015",
         organization = "Image and Signal Processing for Remote Sensing, 21.",
             keywords = "Hyperspectral, image classification, HMM, entropy.",
             abstract = "The Hyperspectral images due to its good spectral resolution are 
                         extensively used for classification, but its high number of bands 
                         requires a higher bandwidth in the transmission data, a higher 
                         data storage capability and a higher computational capability in 
                         processing systems. This work presents a new methodology for 
                         hyperspectral data classification that can work with a reduced 
                         number of spectral bands and achieve good results, comparable with 
                         processing methods that require all hyperspectral bands. The 
                         proposed method for hyperspectral spectra classification is based 
                         on the Hidden Markov Model (HMM) associated to each Endmember (EM) 
                         of a scene and the conditional probabilities of each EM belongs to 
                         each other EM. The EM conditional probability is transformed in EM 
                         vector entropy and those vectors are used as reference vectors for 
                         the classes in the scene. The conditional probability of a 
                         spectrum that will be classified is also transformed in a spectrum 
                         entropy vector, which is classified in a given class by the 
                         minimum ED (Euclidian Distance) among it and the EM entropy 
                         vectors. The methodology was tested with good results using AVIRIS 
                         spectra of a scene with 13 EM considering the full 209 bands and 
                         the reduced spectral bands of 128, 64 and 32. For the test area 
                         its show that can be used only 32 spectral bands instead of the 
                         original 209 bands, without significant loss in the classification 
                         process.",
  conference-location = "Toulouse, France",
      conference-year = "21 Sept.",
                  doi = "10.1117/12.2194135",
                  url = "http://dx.doi.org/10.1117/12.2194135",
           targetfile = "96430P.pdf",
        urlaccessdate = "01 dez. 2020"
}


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