Blind source separation applied to spectral unmixing: comparing different measures of nongaussianity (Articolo in rivista)

Type
Label
  • Blind source separation applied to spectral unmixing: comparing different measures of nongaussianity (Articolo in rivista) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Alternative label
  • Caiafa C. F.; Salerno E.; Proto A. N. (2007)
    Blind source separation applied to spectral unmixing: comparing different measures of nongaussianity
    in Lecture notes in computer science
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Caiafa C. F.; Salerno E.; Proto A. N. (literal)
Pagina inizio
  • 1 (literal)
Pagina fine
  • 8 (literal)
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  • 4694 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: Knowledge-Based Intelligent Information and Engineering Systems. 11th International Conference KES 2007, XVII Italian Workshop on Neural Networks (Vietri sul Mare, 12-14 September 2007). Proceedings, pp. 1 - 8. Bruno Apolloni, Robert J. Howlett and La (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • * Laboratorio de Sistemas Complejos. Facultad de Ingenieria - Universidad de Buenos Aires, Argentina ** Comision de Investigaciones Cientificas de la Prov. de Buenos Aires, Argentina (literal)
Titolo
  • Blind source separation applied to spectral unmixing: comparing different measures of nongaussianity (literal)
Abstract
  • We report some of our results of a particular blind source separation technique applied to spectral unmixing of remote-sensed hyperspectral images. Different nongaussianity measures are introduced in the learning procedure, and the results are compared to assess their relative efficiencies, with respect to both the output signal-to-interference ratio and the overall computational complexity. This study has been conducted on both simulated and real data sets, and the first results show that skewness is a powerful and unexpensive tool to extract the typical sources that charcterize remote-sensed images. (literal)
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