@InProceedings{CarvalhoSantSant:2017:UnClPo,
               author = "Carvalho, Naiallen Carolyne Rodrigues Lima and Sant'Anna, Leonardo 
                         Bins and Sant'Anna, Sidnei Jo{\~a}o Siqueira",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Unsupervised Classification of PolSAR Images using the K-means 
                         algorithm based on stochastic distances",
            booktitle = "Anais...",
                 year = "2017",
         organization = "Workshop dos Cursos de Computa{\c{c}}{\~a}o Aplicada do INPE, 
                         17. (WORCAP)",
             keywords = "Stochastic distance, PolSAR, k-means.",
             abstract = "Nowadays there is a growing gamma of images generated by satellite 
                         that uses SAR Synthetic Aperture Radar) sensors, due to that, many 
                         algorithms have been developed for handle this kind of data. The 
                         SAR systems act in the microwave range and could generate images 
                         in a single polarization, in a single frequency or in multiples 
                         polarizations and multiples frequencies. The images generated by a 
                         mixture of polarizations horizontal and vertical are called PolSAR 
                         (Polarimetric Synthetic Aperture Radar) and are the focus of this 
                         work. The classification of PolSAR images provides a thorough 
                         characterization of the targets allowing a better segmentation of 
                         the area. Image classification consists in separating the data 
                         into groups based on their similarity, and the unsupervised 
                         approach does do that automatically by finding clusters based on a 
                         certain criterion. In this work, we propose to perform an 
                         unsupervised classification method to classify the PolSAR images, 
                         using the k-means algorithm with the statistical approach which 
                         objective is associate a given sample to a cluster according to a 
                         probability distribution, and this association depends on the 
                         stochastic distance of this sample and the center of mass of the 
                         cluster. In general, the Gaussian distribution is the model widely 
                         used, running on several occasions as a standard model for 
                         modeling data, especially when the probability distribution of a 
                         group is not known, but for PolSAR classification the parameter 
                         used is a multilook covariance matrix which obeys the complex 
                         Wishart distribution. Therefore, in this work, we compare five 
                         stochastic distances: Bhattacharyya, Kullback-Leibler, Hellinger, 
                         Renyi of order \β e Chi-square. And the results showed that 
                         the proposed version of K-means reaches higher accuracy values 
                         compared to the classic version, which uses the Euclidian 
                         distance.",
  conference-location = "S{\~a}o Jos{\'e} dos Campos, SP",
      conference-year = "20-22 nov. 2017",
             language = "en",
           targetfile = "Carvalho_unsupervised.pdf",
        urlaccessdate = "02 maio 2024"
}