Cauchy-Rician Model for Backscattering in Urban SAR Images

This letter presents a new statistical model for urban scene synthetic aperture radar (SAR) images by combining the Cauchy distribution, which is heavy tailed, with the Rician backscattering. The literature spans various well-known models most of which are derived under the assumption that the scene...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022, Vol.19, p.1-5
Main Authors: Karakus, Oktay, Kuruoglu, Ercan E., Achim, Alin, Altinkaya, Mustafa A.
Format: Article
Language:English
Subjects:
Backscatter
Backscattering
Cauchy–Rician distribution
Distribution
Mathematical models
Modelling
Numerical analysis
Numerical models
Probability density function
Radar imaging
Radar polarimetry
Reflectors
Rician channels
SAR (radar)
Scattering
Sea surface
Statistical analysis
Statistical methods
Statistical models
Synthetic aperture radar
synthetic aperture radar (SAR) imaging
Urban areas
urban modeling
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Summary:This letter presents a new statistical model for urban scene synthetic aperture radar (SAR) images by combining the Cauchy distribution, which is heavy tailed, with the Rician backscattering. The literature spans various well-known models most of which are derived under the assumption that the scene consists of multitudes of random reflectors. This idea specifically fails for urban scenes since they accommodate a heterogeneous collection of strong scatterers such as buildings, cars, and wall corners. Moreover, when it comes to analyzing their statistical behavior, due to these strong reflectors, urban scenes include a high number of high amplitude samples, which implies that urban scenes are mostly heavy-tailed. The proposed Cauchy-Rician model contributes to the literature by leveraging nonzero location (Rician) heavy-tailed (Cauchy) signal components. In the experimental analysis, the Cauchy-Rician model is investigated in comparison to state-of-the-art statistical models that include \mathcal {G}_{0} , generalized gamma, and the lognormal distribution. The numerical analysis demonstrates the superior performance and flexibility of the proposed distribution for modeling urban scenes.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2022.3146370