This paper proposes a new method for clustering polarimetric synthetic aperture radar images by leveraging the peculiar characteristics of the polarimetric covariance matrix (PCM). Specifically, the feature used for classification is the PCM structure. To this end, the problem of detecting and classifying spatial variations in PCM structure is formulated as a multiple hypothesis test, where one null hypothesis and multiple alternative hypotheses are present. The estimation problems are solved by resorting to hidden random variables representative of covariance structure classes in conjunction with the expectation-maximization algorithm. These estimates are then used to form a penalized likelihood ratio test. The effectiveness of the proposed detection strategies is demonstrated on real polarimetric SAR data.


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    Titel :

    PolSAR Covariance Structure Detection and Classification based on the EM Algorithm


    Beteiligte:
    Han, Sudan (Autor:in) / Addabbo, Pia (Autor:in) / Biondi, Filippo (Autor:in) / Clemente, Carmine (Autor:in) / Orlando, Danilo (Autor:in) / Ricci, Giuseppe (Autor:in)


    Erscheinungsdatum :

    27.06.2022


    Format / Umfang :

    4486312 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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