Robust unsupervised nonparametric change detection of SAR images (Contributo in atti di convegno)

Type
Label
  • Robust unsupervised nonparametric change detection of SAR images (Contributo in atti di convegno) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/IGARSS.2012.6351111 (literal)
Alternative label
  • Andrea Garzelli, Claudia Zoppetti, Bruno Aiazzi, Stefano Baronti, Luciano Alparone (2012)
    Robust unsupervised nonparametric change detection of SAR images
    in IEEE IGARSS 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium, Monaco di Baviera, Germania, 22-27 Luglio 2012
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Andrea Garzelli, Claudia Zoppetti, Bruno Aiazzi, Stefano Baronti, Luciano Alparone (literal)
Pagina inizio
  • 1988 (literal)
Pagina fine
  • 1991 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6351111&contentType=Conference+Publications&refinements%3D4294595554%2C4282331638%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6350328%29 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • Proceedings of IEEE IGARSS 2012: Remote Sensing for a Dynamic Earth (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 4 (literal)
Note
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Dept. Information Engineering, University of Siena, 53100 Siena, Italy Dept. Information Engineering, University of Siena, 53100 Siena, Italy IFAC-CNR, Research Area of Florence, 50019, Florence, Italy IFAC-CNR, Research Area of Florence, 50019, Florence, Italy Dept. Electron. & Telecomm, University of Florence, 50139 Florence, Italy (literal)
Titolo
  • Robust unsupervised nonparametric change detection of SAR images (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4673-1158-8 (literal)
Abstract
  • This paper presents an unsupervised nonparametric method for change detection in multitemporal synthetic aperture radar (SAR) imagery. The proposed method relies on a novel feature capable of capturing the structural changes between the two images and discarding almost completely the statistical changes due to speckle patterns or co-registration inaccuracies. This feature utilizes the scatterplots of the amplitude levels in the two SAR images and applies a fast version of the mean-shift (MS) algorithm to find the modes of the underlying bivariate distribution. The value of the probability density function (PDF) is translated to a value of conditional information and given to all image pixels originating such modes. Experimental results have been carried out with simulated changes and true SAR images acquired by the COSMO-SkyMed satellite constellation. The proposed feature exhibits significantly better discrimination capability than both the classical log-ratio (LR) and is particularly robust if applied to SAR images having different processing and/or acquisition angles. (literal)
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