MODELING SAR IMAGES BASED ON A GENERALIZED GAMMA DISTRIBUTION FOR TEXTURE COMPONENT

doi:10.2528/PIER13011807

MODELING SAR IMAGES BASED ON A GENERALIZED GAMMA DISTRIBUTION FOR TEXTURE COMPONENT

2013-01-01

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DOI：10.2528/PIER13011807

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In the applications of synthetic aperture radar (SAR) data, a crucial problem is to develop precise models for the statistics of the pixel amplitudes or intensities. In this paper, a new statistical model, called simply here GFF, is proposed based on the product model by assuming the radar cross section (RCS) components (texture components) of the return obey a recently empirical generalized Gamma distribution. Meanwhile, we demonstrate theoretically that the proposed GFF model has the well-known IC and g distributions as special cases. We also derived analytically the estimators of the presented GFF model by applying the method-of-log-cumulants (MoLC). Finally, the performance of the proposed model is tested by using some measured SAR images.

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SYNTHETIC-APERTURE RADAR

ELECTROMAGNETIC SCATTERING

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MODELING SAR IMAGES BASED ON A GENERALIZED GAMMA DISTRIBUTION FOR TEXTURE COMPONENT

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